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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Paris, S"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" 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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.12037">arXiv:2501.12037</a> <span> [<a href="https://arxiv.org/pdf/2501.12037">pdf</a>, <a href="https://arxiv.org/format/2501.12037">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> A Stochastic Geometry Based Techno-Economic Analysis of RIS-Assisted Cellular Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+G">Guodong Sun</a>, <a href="/search/cs?searchtype=author&query=Baccelli%2C+F">Francois Baccelli</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+L+U">Luis Uzeda Garcia</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.12037v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surfaces (RISs) are a promising technology for enhancing cellular network performance and yielding additional value to network operators. This paper proposes a techno-economic analysis of RIS-assisted cellular networks to guide operators in deciding between deploying additional RISs or base stations (BS). We assume a relative cost model that considers the total cost of o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12037v1-abstract-full').style.display = 'inline'; document.getElementById('2501.12037v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.12037v1-abstract-full" style="display: none;"> Reconfigurable intelligent surfaces (RISs) are a promising technology for enhancing cellular network performance and yielding additional value to network operators. This paper proposes a techno-economic analysis of RIS-assisted cellular networks to guide operators in deciding between deploying additional RISs or base stations (BS). We assume a relative cost model that considers the total cost of ownership (TCO) of deploying additional nodes, either BSs or RISs. We assume a return on investment (RoI) that is proportional to the system's spectral efficiency. The latter is evaluated based on a stochastic geometry model that gives an integral formula for the ergodic rate in cellular networks equipped with RISs. The marginal RoI for any investment strategy is determined by the partial derivative of this integral expression with respect to node densities. We investigate two case studies: throughput enhancement and coverage hole mitigation. These examples demonstrate how operators could determine the optimal investment strategy in scenarios defined by the current densities of BSs and RISs, and their relative costs. Numerical results illustrate the evolution of ergodic rates based on the proposed investment strategy, demonstrating the investment decision-making process while considering technological and economic factors. This work quantitatively demonstrates that strategically investing in RISs can offer better system-level benefits than solely investing in BS densification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12037v1-abstract-full').style.display = 'none'; document.getElementById('2501.12037v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This document supports a work submitted to WiOpt2025, including the supplementary mathematical verification in the appendix</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.00741">arXiv:2412.00741</a> <span> [<a href="https://arxiv.org/pdf/2412.00741">pdf</a>, <a href="https://arxiv.org/format/2412.00741">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Overview of NR Enhancements for Extended Reality (XR) in 3GPP 5G-Advanced </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gapeyenko%2C+M">Margarita Gapeyenko</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Isomaki%2C+M">Markus Isomaki</a>, <a href="/search/cs?searchtype=author&query=Yanakiev%2C+B">Boyan Yanakiev</a>, <a href="/search/cs?searchtype=author&query=Amiri%2C+A">Abolfazl Amiri</a>, <a href="/search/cs?searchtype=author&query=S%C3%A9bire%2C+B">Benoist S茅bire</a>, <a href="/search/cs?searchtype=author&query=Kaikkonen%2C+J">Jorma Kaikkonen</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+C">Chunli Wu</a>, <a href="/search/cs?searchtype=author&query=Pedersen%2C+K+I">Klaus I. Pedersen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.00741v1-abstract-short" style="display: inline;"> Extended reality (XR) is unlocking numerous possibilities and continues attracting individuals and larger groups across different business sectors. With Virtual reality (VR), Augmented reality (AR), or Mixed reality (MR) it is possible to improve the way we access, deliver and exchange information in education, health care, entertainment, and many other aspects of our daily lives. However, to full… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00741v1-abstract-full').style.display = 'inline'; document.getElementById('2412.00741v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.00741v1-abstract-full" style="display: none;"> Extended reality (XR) is unlocking numerous possibilities and continues attracting individuals and larger groups across different business sectors. With Virtual reality (VR), Augmented reality (AR), or Mixed reality (MR) it is possible to improve the way we access, deliver and exchange information in education, health care, entertainment, and many other aspects of our daily lives. However, to fully exploit the potential of XR, it is important to provide reliable, fast and secure wireless connectivity to the users of XR and that requires refining existing solutions and tailoring those to support XR services. This article presents a tutorial on 3GPP 5G-Advanced Release 18 XR activities, summarizing physical as well as higher layer enhancements introduced for New Radio considering the specifics of XR. In addition, we also describe enhancements across 5G system architecture that impacted radio access network. Furthermore, the paper provides system-level simulation results for several Release 18 enhancements to show their benefits in terms of XR capacity and power saving gains. Finally, it concludes with an overview of future work in Release 19 that continues developing features to support XR services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00741v1-abstract-full').style.display = 'none'; document.getElementById('2412.00741v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13687">arXiv:2409.13687</a> <span> [<a href="https://arxiv.org/pdf/2409.13687">pdf</a>, <a href="https://arxiv.org/format/2409.13687">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A Bottom-Up Approach to Class-Agnostic Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dille%2C+S">Sebastian Dille</a>, <a href="/search/cs?searchtype=author&query=Blondal%2C+A">Ari Blondal</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Aksoy%2C+Y">Ya臒谋z Aksoy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.13687v1-abstract-short" style="display: inline;"> Class-agnostic image segmentation is a crucial component in automating image editing workflows, especially in contexts where object selection traditionally involves interactive tools. Existing methods in the literature often adhere to top-down formulations, following the paradigm of class-based approaches, where object detection precedes per-object segmentation. In this work, we present a novel bo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13687v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13687v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13687v1-abstract-full" style="display: none;"> Class-agnostic image segmentation is a crucial component in automating image editing workflows, especially in contexts where object selection traditionally involves interactive tools. Existing methods in the literature often adhere to top-down formulations, following the paradigm of class-based approaches, where object detection precedes per-object segmentation. In this work, we present a novel bottom-up formulation for addressing the class-agnostic segmentation problem. We supervise our network directly on the projective sphere of its feature space, employing losses inspired by metric learning literature as well as losses defined in a novel segmentation-space representation. The segmentation results are obtained through a straightforward mean-shift clustering of the estimated features. Our bottom-up formulation exhibits exceptional generalization capability, even when trained on datasets designed for class-based segmentation. We further showcase the effectiveness of our generic approach by addressing the challenging task of cell and nucleus segmentation. We believe that our bottom-up formulation will offer valuable insights into diverse segmentation challenges in the literature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13687v1-abstract-full').style.display = 'none'; document.getElementById('2409.13687v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.05967">arXiv:2405.05967</a> <span> [<a href="https://arxiv.org/pdf/2405.05967">pdf</a>, <a href="https://arxiv.org/format/2405.05967">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Distilling Diffusion Models into Conditional GANs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+M">Minguk Kang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Richard Zhang</a>, <a href="/search/cs?searchtype=author&query=Barnes%2C+C">Connelly Barnes</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+S">Suha Kwak</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jaesik Park</a>, <a href="/search/cs?searchtype=author&query=Shechtman%2C+E">Eli Shechtman</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+J">Jun-Yan Zhu</a>, <a href="/search/cs?searchtype=author&query=Park%2C+T">Taesung Park</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.05967v3-abstract-short" style="display: inline;"> We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality. Our approach interprets diffusion distillation as a paired image-to-image translation task, using noise-to-image pairs of the diffusion model's ODE trajectory. For efficient regression loss computation, we propose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.05967v3-abstract-full').style.display = 'inline'; document.getElementById('2405.05967v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.05967v3-abstract-full" style="display: none;"> We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality. Our approach interprets diffusion distillation as a paired image-to-image translation task, using noise-to-image pairs of the diffusion model's ODE trajectory. For efficient regression loss computation, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space, utilizing an ensemble of augmentations. Furthermore, we adapt a diffusion model to construct a multi-scale discriminator with a text alignment loss to build an effective conditional GAN-based formulation. E-LatentLPIPS converges more efficiently than many existing distillation methods, even accounting for dataset construction costs. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models -- DMD, SDXL-Turbo, and SDXL-Lightning -- on the zero-shot COCO benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.05967v3-abstract-full').style.display = 'none'; document.getElementById('2405.05967v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project page: https://mingukkang.github.io/Diffusion2GAN/ (ECCV2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.08969">arXiv:2311.08969</a> <span> [<a href="https://arxiv.org/pdf/2311.08969">pdf</a>, <a href="https://arxiv.org/format/2311.08969">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> PDU-set Scheduling Algorithm for XR Traffic in Multi-Service 5G-Advanced Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Paymard%2C+P">Pouria Paymard</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Amiri%2C+A">Abolfazl Amiri</a>, <a href="/search/cs?searchtype=author&query=Kolding%2C+T+E">Troels E. Kolding</a>, <a href="/search/cs?searchtype=author&query=Moya%2C+F+S">Fernando Sanchez Moya</a>, <a href="/search/cs?searchtype=author&query=Pedersen%2C+K+I">Klaus I. Pedersen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.08969v1-abstract-short" style="display: inline;"> In this paper, we investigate a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, multiple users, and multiple services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.08969v1-abstract-full').style.display = 'inline'; document.getElementById('2311.08969v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.08969v1-abstract-full" style="display: none;"> In this paper, we investigate a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, multiple users, and multiple services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performance of the proposed solution, advanced dynamic system-level simulations are conducted. The findings reveal that the proposed scheduler offers a notable improvement in increasing XR capacity up to 45%, while keeping the same enhanced mobile broadband (eMBB) cell throughput as compared to the well-known baseline schedulers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.08969v1-abstract-full').style.display = 'none'; document.getElementById('2311.08969v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.06754">arXiv:2310.06754</a> <span> [<a href="https://arxiv.org/pdf/2310.06754">pdf</a>, <a href="https://arxiv.org/format/2310.06754">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> A Stochastic Geometry Framework for Performance Analysis of RIS-assisted OFDM Cellular Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+G">Guodong Sun</a>, <a href="/search/cs?searchtype=author&query=Baccelli%2C+F">Francois Baccelli</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+K">Ke Feng</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+L+U">Luis Uzeda Garcia</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.06754v4-abstract-short" style="display: inline;"> The reconfigurable intelligent surface (RIS) technology allows one to engineer spatial diversity in complex cellular networks. This paper provides a framework for the system-level performance assessment of RIS-assisted networks and in particular downlink coverage probability and ergodic rate. To account for the inherent randomness in the spatial deployments of base stations (BSs) and RISs, we mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06754v4-abstract-full').style.display = 'inline'; document.getElementById('2310.06754v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.06754v4-abstract-full" style="display: none;"> The reconfigurable intelligent surface (RIS) technology allows one to engineer spatial diversity in complex cellular networks. This paper provides a framework for the system-level performance assessment of RIS-assisted networks and in particular downlink coverage probability and ergodic rate. To account for the inherent randomness in the spatial deployments of base stations (BSs) and RISs, we model the placements of the RISs as point processes (PPs) conditioned on the associated BSs, which are modeled by a Poisson point process (PPP). These RIS PPs can be adapted based on the deployment strategy. We focus on modeling the RISs as a Mat茅rn cluster process (MCP), where each RIS cluster is a finite PPP with support a disc centered on the association BS. We assume that the system uses the orthogonal frequency division multiplexing (OFDM) technique to exploit the multipath diversity provided by RISs. The coverage probability and the ergodic rate can be evaluated when RISs operate as batched powerless beamformers. The resulting analytical expressions provide a general methodology to evaluate the impact of key RIS-related parameters, such as the batch size and the density of RISs, on system-level performance. To demonstrate the framework's broad applicability, we also analyze a RIS placement variant where RISs are deployed around coverage holes. Numerical evaluations of the analytical expressions and Monte-Carlo simulations jointly validate the proposed analytical approach and provide valuable insights into the design of future RIS-assisted cellular networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06754v4-abstract-full').style.display = 'none'; document.getElementById('2310.06754v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.00491">arXiv:2310.00491</a> <span> [<a href="https://arxiv.org/pdf/2310.00491">pdf</a>, <a href="https://arxiv.org/format/2310.00491">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jain%2C+G">Gaurav Jain</a>, <a href="/search/cs?searchtype=author&query=Hindi%2C+B">Basel Hindi</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zihao Zhang</a>, <a href="/search/cs?searchtype=author&query=Srinivasula%2C+K">Koushik Srinivasula</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+M">Mingyu Xie</a>, <a href="/search/cs?searchtype=author&query=Ghasemi%2C+M">Mahshid Ghasemi</a>, <a href="/search/cs?searchtype=author&query=Weiner%2C+D">Daniel Weiner</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S+A">Sophie Ana Paris</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+X+Y+T">Xin Yi Therese Xu</a>, <a href="/search/cs?searchtype=author&query=Malcolm%2C+M">Michael Malcolm</a>, <a href="/search/cs?searchtype=author&query=Turkcan%2C+M">Mehmet Turkcan</a>, <a href="/search/cs?searchtype=author&query=Ghaderi%2C+J">Javad Ghaderi</a>, <a href="/search/cs?searchtype=author&query=Kostic%2C+Z">Zoran Kostic</a>, <a href="/search/cs?searchtype=author&query=Zussman%2C+G">Gil Zussman</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+B+A">Brian A. Smith</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.00491v2-abstract-short" style="display: inline;"> Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrum… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00491v2-abstract-full').style.display = 'inline'; document.getElementById('2310.00491v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.00491v2-abstract-full" style="display: none;"> Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera's video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00491v2-abstract-full').style.display = 'none'; document.getElementById('2310.00491v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.05511">arXiv:2303.05511</a> <span> [<a href="https://arxiv.org/pdf/2303.05511">pdf</a>, <a href="https://arxiv.org/format/2303.05511">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Scaling up GANs for Text-to-Image Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+M">Minguk Kang</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+J">Jun-Yan Zhu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Richard Zhang</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jaesik Park</a>, <a href="/search/cs?searchtype=author&query=Shechtman%2C+E">Eli Shechtman</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Park%2C+T">Taesung Park</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.05511v2-abstract-short" style="display: inline;"> The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05511v2-abstract-full').style.display = 'inline'; document.getElementById('2303.05511v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.05511v2-abstract-full" style="display: none;"> The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that na脧vely increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05511v2-abstract-full').style.display = 'none'; document.getElementById('2303.05511v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">CVPR 2023. Project webpage at https://mingukkang.github.io/GigaGAN/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.13215">arXiv:2203.13215</a> <span> [<a href="https://arxiv.org/pdf/2203.13215">pdf</a>, <a href="https://arxiv.org/format/2203.13215">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> </div> </div> <p class="title is-5 mathjax"> Neural Neighbor Style Transfer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kolkin%2C+N">Nicholas Kolkin</a>, <a href="/search/cs?searchtype=author&query=Kucera%2C+M">Michal Kucera</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Sykora%2C+D">Daniel Sykora</a>, <a href="/search/cs?searchtype=author&query=Shechtman%2C+E">Eli Shechtman</a>, <a href="/search/cs?searchtype=author&query=Shakhnarovich%2C+G">Greg Shakhnarovich</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.13215v1-abstract-short" style="display: inline;"> We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.13215v1-abstract-full').style.display = 'inline'; document.getElementById('2203.13215v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.13215v1-abstract-full" style="display: none;"> We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the spirit of our approach is similar to prior work, we show that our design decisions dramatically improve the final visual quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.13215v1-abstract-full').style.display = 'none'; document.getElementById('2203.13215v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Code for NNST-Opt available at https://github.com/nkolkin13/NeuralNeighborStyleTransfer</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.02242">arXiv:2203.02242</a> <span> [<a href="https://arxiv.org/pdf/2203.02242">pdf</a>, <a href="https://arxiv.org/format/2203.02242">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radio </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gapeyenko%2C+M">Margarita Gapeyenko</a>, <a href="/search/cs?searchtype=author&query=Petrov%2C+V">Vitaly Petrov</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Marcano%2C+A">Andrea Marcano</a>, <a href="/search/cs?searchtype=author&query=Pedersen%2C+K+I">Klaus I. Pedersen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.02242v3-abstract-short" style="display: inline;"> Extended Reality (XR) is one of the major innovations to be introduced in 5G/5G-Advanced communication systems. A combination of augmented reality, virtual reality, and mixed reality, supplemented by cloud gaming, revisits the way how humans interact with computers, networks, and each other. However, efficient support of XR services imposes new challenges for existing and future wireless networks.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.02242v3-abstract-full').style.display = 'inline'; document.getElementById('2203.02242v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.02242v3-abstract-full" style="display: none;"> Extended Reality (XR) is one of the major innovations to be introduced in 5G/5G-Advanced communication systems. A combination of augmented reality, virtual reality, and mixed reality, supplemented by cloud gaming, revisits the way how humans interact with computers, networks, and each other. However, efficient support of XR services imposes new challenges for existing and future wireless networks. This article presents a tutorial on integrating support for the XR into the 3GPP New Radio (NR), summarizing a range of activities handled within various 3GPP Service and Systems Aspects (SA) and Radio Access Networks (RAN) groups. The article also delivers a case study evaluating the performance of different XR services in state-of-the-art NR Release 17. The paper concludes with a vision of further enhancements to better support XR in future NR releases and outlines open problems in this area. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.02242v3-abstract-full').style.display = 'none'; document.getElementById('2203.02242v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to IEEE Network, 2023. Copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material, creating new works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.14021">arXiv:2105.14021</a> <span> [<a href="https://arxiv.org/pdf/2105.14021">pdf</a>, <a href="https://arxiv.org/format/2105.14021">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Miangoleh%2C+S+M+H">S. Mahdi H. Miangoleh</a>, <a href="/search/cs?searchtype=author&query=Dille%2C+S">Sebastian Dille</a>, <a href="/search/cs?searchtype=author&query=Mai%2C+L">Long Mai</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Aksoy%2C+Y">Ya臒谋z Aksoy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2105.14021v1-abstract-short" style="display: inline;"> Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.14021v1-abstract-full').style.display = 'inline'; document.getElementById('2105.14021v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.14021v1-abstract-full" style="display: none;"> Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between a consistent scene structure and the high-frequency details, and merge low- and high-resolution estimations to take advantage of this duality using a simple depth merging network. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details to the final result. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail using a pre-trained model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.14021v1-abstract-full').style.display = 'none'; document.getElementById('2105.14021v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">For more details visit http://yaksoy.github.io/highresdepth/</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proc. CVPR (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.10023">arXiv:2006.10023</a> <span> [<a href="https://arxiv.org/pdf/2006.10023">pdf</a>, <a href="https://arxiv.org/format/2006.10023">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Analytical Probability Distributions and EM-Learning for Deep Generative Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Balestriero%2C+R">Randall Balestriero</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sebastien Paris</a>, <a href="/search/cs?searchtype=author&query=Baraniuk%2C+R+G">Richard G. Baraniuk</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.10023v1-abstract-short" style="display: inline;"> Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known analytical form for the posterior and likelihood expectation, VAEs resort to approximations, including (Amortized) Variational Inference (AVI) and Monte-Carlo (MC) sampling. We exploit the Continuous Piecewise Affine (CPA)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.10023v1-abstract-full').style.display = 'inline'; document.getElementById('2006.10023v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.10023v1-abstract-full" style="display: none;"> Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known analytical form for the posterior and likelihood expectation, VAEs resort to approximations, including (Amortized) Variational Inference (AVI) and Monte-Carlo (MC) sampling. We exploit the Continuous Piecewise Affine (CPA) property of modern DGNs to derive their posterior and marginal distributions as well as the latter's first moments. These findings enable us to derive an analytical Expectation-Maximization (EM) algorithm that enables gradient-free DGN learning. We demonstrate empirically that EM training of DGNs produces greater likelihood than VAE training. Our findings will guide the design of new VAE AVI that better approximate the true posterior and open avenues to apply standard statistical tools for model comparison, anomaly detection, and missing data imputation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.10023v1-abstract-full').style.display = 'none'; document.getElementById('2006.10023v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.01703">arXiv:2005.01703</a> <span> [<a href="https://arxiv.org/pdf/2005.01703">pdf</a>, <a href="https://arxiv.org/format/2005.01703">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Transforming and Projecting Images into Class-conditional Generative Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huh%2C+M">Minyoung Huh</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Richard Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+J">Jun-Yan Zhu</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Hertzmann%2C+A">Aaron Hertzmann</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.01703v2-abstract-short" style="display: inline;"> We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01703v2-abstract-full').style.display = 'inline'; document.getElementById('2005.01703v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.01703v2-abstract-full" style="display: none;"> We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias and color bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better editability of these images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01703v2-abstract-full').style.display = 'none'; document.getElementById('2005.01703v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ECCV2020 (oral)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.02546">arXiv:2004.02546</a> <span> [<a href="https://arxiv.org/pdf/2004.02546">pdf</a>, <a href="https://arxiv.org/format/2004.02546">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> </div> </div> <p class="title is-5 mathjax"> GANSpace: Discovering Interpretable GAN Controls </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=H%C3%A4rk%C3%B6nen%2C+E">Erik H盲rk枚nen</a>, <a href="/search/cs?searchtype=author&query=Hertzmann%2C+A">Aaron Hertzmann</a>, <a href="/search/cs?searchtype=author&query=Lehtinen%2C+J">Jaakko Lehtinen</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.02546v3-abstract-short" style="display: inline;"> This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable control… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.02546v3-abstract-full').style.display = 'inline'; document.getElementById('2004.02546v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.02546v3-abstract-full" style="display: none;"> This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.02546v3-abstract-full').style.display = 'none'; document.getElementById('2004.02546v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to NeurIPS 2020</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 9841-9850 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.11912">arXiv:2002.11912</a> <span> [<a href="https://arxiv.org/pdf/2002.11912">pdf</a>, <a href="https://arxiv.org/format/2002.11912">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Max-Affine Spline Insights into Deep Generative Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Balestriero%2C+R">Randall Balestriero</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sebastien Paris</a>, <a href="/search/cs?searchtype=author&query=Baraniuk%2C+R">Richard Baraniuk</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.11912v1-abstract-short" style="display: inline;"> We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the latent space partition, dimension and angularity of the generated manifold, we relate the manifold dimension and approximation error to the sample size. The manifold-per-region affine subspace defines a local coordinate bas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.11912v1-abstract-full').style.display = 'inline'; document.getElementById('2002.11912v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.11912v1-abstract-full" style="display: none;"> We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the latent space partition, dimension and angularity of the generated manifold, we relate the manifold dimension and approximation error to the sample size. The manifold-per-region affine subspace defines a local coordinate basis; we provide necessary and sufficient conditions relating those basis vectors with disentanglement. We also derive the output probability density mapped onto the generated manifold in terms of the latent space density, which enables the computation of key statistics such as its Shannon entropy. This finding also enables the computation of the GDN likelihood, which provides a new mechanism for model comparison as well as providing a quality measure for (generated) samples under the learned distribution. We demonstrate how low entropy and/or multimodal distributions are not naturally modeled by DGNs and are a cause of training instabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.11912v1-abstract-full').style.display = 'none'; document.getElementById('2002.11912v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.03189">arXiv:1804.03189</a> <span> [<a href="https://arxiv.org/pdf/1804.03189">pdf</a>, <a href="https://arxiv.org/format/1804.03189">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> </div> </div> <p class="title is-5 mathjax"> Deep Painterly Harmonization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luan%2C+F">Fujun Luan</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Shechtman%2C+E">Eli Shechtman</a>, <a href="/search/cs?searchtype=author&query=Bala%2C+K">Kavita Bala</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1804.03189v4-abstract-short" style="display: inline;"> Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.03189v4-abstract-full').style.display = 'inline'; document.getElementById('1804.03189v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.03189v4-abstract-full" style="display: none;"> Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.03189v4-abstract-full').style.display = 'none'; document.getElementById('1804.03189v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.05651">arXiv:1712.05651</a> <span> [<a href="https://arxiv.org/pdf/1712.05651">pdf</a>, <a href="https://arxiv.org/format/1712.05651">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.23919/IFIPNetworking.2018.8696406">10.23919/IFIPNetworking.2018.8696406 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Adaptive Robust Traffic Engineering in Software Defined Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sanvito%2C+D">Davide Sanvito</a>, <a href="/search/cs?searchtype=author&query=Filippini%2C+I">Ilario Filippini</a>, <a href="/search/cs?searchtype=author&query=Capone%2C+A">Antonio Capone</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Leguay%2C+J">Jeremie Leguay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1712.05651v1-abstract-short" style="display: inline;"> One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05651v1-abstract-full').style.display = 'inline'; document.getElementById('1712.05651v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.05651v1-abstract-full" style="display: none;"> One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05651v1-abstract-full').style.display = 'none'; document.getElementById('1712.05651v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 8 figures, submitted to IFIP Networking 2018</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IFIP Networking 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.07511">arXiv:1703.07511</a> <span> [<a href="https://arxiv.org/pdf/1703.07511">pdf</a>, <a href="https://arxiv.org/format/1703.07511">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Deep Photo Style Transfer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luan%2C+F">Fujun Luan</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Shechtman%2C+E">Eli Shechtman</a>, <a href="/search/cs?searchtype=author&query=Bala%2C+K">Kavita Bala</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.07511v3-abstract-short" style="display: inline;"> This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photoreal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07511v3-abstract-full').style.display = 'inline'; document.getElementById('1703.07511v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.07511v3-abstract-full" style="display: none;"> This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07511v3-abstract-full').style.display = 'none'; document.getElementById('1703.07511v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.09011">arXiv:1701.09011</a> <span> [<a href="https://arxiv.org/pdf/1701.09011">pdf</a>, <a href="https://arxiv.org/format/1701.09011">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Overlay Routing for Fast Video Transfers in CDN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Medagliani%2C+P">Paolo Medagliani</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Leguay%2C+J">J茅r茅mie Leguay</a>, <a href="/search/cs?searchtype=author&query=Maggi%2C+L">Lorenzo Maggi</a>, <a href="/search/cs?searchtype=author&query=Chuangsong%2C+X">Xue Chuangsong</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+H">Haojun Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1701.09011v1-abstract-short" style="display: inline;"> Content Delivery Networks (CDN) are witnessing the outburst of video streaming (e.g., personal live streaming or Video-on-Demand) where the video content, produced or accessed by mobile phones, must be quickly transferred from a point to another of the network. Whenever a user requests a video not directly available at the edge server, the CDN network must 1) identify the best location in the netw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.09011v1-abstract-full').style.display = 'inline'; document.getElementById('1701.09011v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.09011v1-abstract-full" style="display: none;"> Content Delivery Networks (CDN) are witnessing the outburst of video streaming (e.g., personal live streaming or Video-on-Demand) where the video content, produced or accessed by mobile phones, must be quickly transferred from a point to another of the network. Whenever a user requests a video not directly available at the edge server, the CDN network must 1) identify the best location in the network where the content is stored, 2) set up a connection and 3) deliver the video as quickly as possible. For this reason, existing CDNs are adopting an overlay structure to reduce latency, leveraging the flexibility introduced by the Software Defined Networking (SDN) paradigm. In order to guarantee a satisfactory Quality of Experience (QoE) to users, the connection must respect several Quality of Service (QoS) constraints. In this paper, we focus on the sub-problem 2), by presenting an approach to efficiently compute and maintain paths in the overlay network. Our approach allows to speed up the transfer of video segments by finding minimum delay overlay paths under constraints on hop count, jitter, packet loss and relay processing capacity. The proposed algorithm provides a near-optimal solution, while drastically reducing the execution time. We show on traces collected in a real CDN that our solution allows to maximize the number of fast video transfers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.09011v1-abstract-full').style.display = 'none'; document.getElementById('1701.09011v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1602.01629">arXiv:1602.01629</a> <span> [<a href="https://arxiv.org/pdf/1602.01629">pdf</a>, <a href="https://arxiv.org/format/1602.01629">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Online and Global Network Optimization: Towards the Next-Generation of Routing Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Leguay%2C+J">J茅r茅mie Leguay</a>, <a href="/search/cs?searchtype=author&query=Draief%2C+M">Moez Draief</a>, <a href="/search/cs?searchtype=author&query=Chouvardas%2C+S">Symeon Chouvardas</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a>, <a href="/search/cs?searchtype=author&query=Paschos%2C+G+S">Georgios S. Paschos</a>, <a href="/search/cs?searchtype=author&query=Maggi%2C+L">Lorenzo Maggi</a>, <a href="/search/cs?searchtype=author&query=Qi%2C+M">Meiyu Qi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1602.01629v1-abstract-short" style="display: inline;"> The computation power of SDN controllers fosters the development of a new generation of control plane that uses compute-intensive operations to automate and optimize the network configuration across layers. From now on, cutting-edge optimization and machine learning algorithms can be used to control networks in real-time. This formidable opportunity transforms the way routing systems should be con… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01629v1-abstract-full').style.display = 'inline'; document.getElementById('1602.01629v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1602.01629v1-abstract-full" style="display: none;"> The computation power of SDN controllers fosters the development of a new generation of control plane that uses compute-intensive operations to automate and optimize the network configuration across layers. From now on, cutting-edge optimization and machine learning algorithms can be used to control networks in real-time. This formidable opportunity transforms the way routing systems should be conceived and designed. This paper presents a candidate architecture for the next generation of routing platforms built on three main pillars for admission control, re-routing and monitoring that would have not been possible in legacy control planes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01629v1-abstract-full').style.display = 'none'; document.getElementById('1602.01629v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 6 figures, Under submission</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1512.04354">arXiv:1512.04354</a> <span> [<a href="https://arxiv.org/pdf/1512.04354">pdf</a>, <a href="https://arxiv.org/format/1512.04354">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/QoMEX.2012.6263876">10.1109/QoMEX.2012.6263876 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Paris%2C+S">St茅fane Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1512.04354v1-abstract-short" style="display: inline;"> This short paper presents a perspective plan to build a null reference image quality assessment. Its main goal is to deliver both the objective score and the distortion map for a given distorted image without the knowledge of its reference image. </span> <span class="abstract-full has-text-grey-dark mathjax" id="1512.04354v1-abstract-full" style="display: none;"> This short paper presents a perspective plan to build a null reference image quality assessment. Its main goal is to deliver both the objective score and the distortion map for a given distorted image without the knowledge of its reference image. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1512.04354v1-abstract-full').style.display = 'none'; document.getElementById('1512.04354v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">International Workshop on Quality of Multimedia Experience, 2012, Melbourne, Australia</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1412.7725">arXiv:1412.7725</a> <span> [<a href="https://arxiv.org/pdf/1412.7725">pdf</a>, <a href="https://arxiv.org/format/1412.7725">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Automatic Photo Adjustment Using Deep Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yan%2C+Z">Zhicheng Yan</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+H">Hao Zhang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+B">Baoyuan Wang</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sylvain Paris</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+Y">Yizhou Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1412.7725v2-abstract-short" style="display: inline;"> Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work but such an algorithm faces many… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1412.7725v2-abstract-full').style.display = 'inline'; document.getElementById('1412.7725v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1412.7725v2-abstract-full" style="display: none;"> Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Because of these characteristics, existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep machine learning has shown unique abilities to address hard problems that resisted machine algorithms for long. This motivated us to explore the use of deep learning in the context of photo editing. In this paper, we explain how to formulate the automatic photo adjustment problem in a way suitable for this approach. We also introduce an image descriptor that accounts for the local semantics of an image. Our experiments demonstrate that our deep learning formulation applied using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on the image semantics. We show on several examples that this yields results that are qualitatively and quantitatively better than previous work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1412.7725v2-abstract-full').style.display = 'none'; document.getElementById('1412.7725v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2015; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 December, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">TOG minor revision</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1405.1964">arXiv:1405.1964</a> <span> [<a href="https://arxiv.org/pdf/1405.1964">pdf</a>, <a href="https://arxiv.org/ps/1405.1964">ps</a>, <a href="https://arxiv.org/format/1405.1964">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> A Distributed Demand-Side Management Framework for the Smart Grid </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbato%2C+A">Antimo Barbato</a>, <a href="/search/cs?searchtype=author&query=Capone%2C+A">Antonio Capone</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lin Chen</a>, <a href="/search/cs?searchtype=author&query=Martignon%2C+F">Fabio Martignon</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Stefano Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1405.1964v1-abstract-short" style="display: inline;"> This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1405.1964v1-abstract-full').style.display = 'inline'; document.getElementById('1405.1964v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1405.1964v1-abstract-full" style="display: none;"> This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1405.1964v1-abstract-full').style.display = 'none'; document.getElementById('1405.1964v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 May, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2014. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1306.3058">arXiv:1306.3058</a> <span> [<a href="https://arxiv.org/pdf/1306.3058">pdf</a>, <a href="https://arxiv.org/format/1306.3058">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Physeter catodon localization by sparse coding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Paris%2C+S">S茅bastien Paris</a>, <a href="/search/cs?searchtype=author&query=Doh%2C+Y">Yann Doh</a>, <a href="/search/cs?searchtype=author&query=Glotin%2C+H">Herv茅 Glotin</a>, <a href="/search/cs?searchtype=author&query=Halkias%2C+X">Xanadu Halkias</a>, <a href="/search/cs?searchtype=author&query=Razik%2C+J">Joseph Razik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1306.3058v1-abstract-short" style="display: inline;"> This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea is to regress supervisely from these local features two rough estimates of the d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.3058v1-abstract-full').style.display = 'inline'; document.getElementById('1306.3058v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1306.3058v1-abstract-full" style="display: none;"> This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea is to regress supervisely from these local features two rough estimates of the distance and azimuth thanks to some datasets where both acoustic events and ground-truth position are now available. Furthermore, these estimates can feed a particle filter system in order to obtain a precise spermwhale' position even in mono-hydrophone configuration. Anti-collision system and whale watching are considered applications of this work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.3058v1-abstract-full').style.display = 'none'; document.getElementById('1306.3058v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 June, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 6 figures, workshop ICML4B in ICML 2013 conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1301.3533">arXiv:1301.3533</a> <span> [<a href="https://arxiv.org/pdf/1301.3533">pdf</a>, <a href="https://arxiv.org/format/1301.3533">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Halkias%2C+X">Xanadu Halkias</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S">Sebastien Paris</a>, <a href="/search/cs?searchtype=author&query=Glotin%2C+H">Herve Glotin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1301.3533v2-abstract-short" style="display: inline;"> Deep Belief Networks (DBN) have been successfully applied on popular machine learning tasks. Specifically, when applied on hand-written digit recognition, DBNs have achieved approximate accuracy rates of 98.8%. In an effort to optimize the data representation achieved by the DBN and maximize their descriptive power, recent advances have focused on inducing sparse constraints at each layer of the D… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1301.3533v2-abstract-full').style.display = 'inline'; document.getElementById('1301.3533v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1301.3533v2-abstract-full" style="display: none;"> Deep Belief Networks (DBN) have been successfully applied on popular machine learning tasks. Specifically, when applied on hand-written digit recognition, DBNs have achieved approximate accuracy rates of 98.8%. In an effort to optimize the data representation achieved by the DBN and maximize their descriptive power, recent advances have focused on inducing sparse constraints at each layer of the DBN. In this paper we present a theoretical approach for sparse constraints in the DBN using the mixed norm for both non-overlapping and overlapping groups. We explore how these constraints affect the classification accuracy for digit recognition in three different datasets (MNIST, USPS, RIMES) and provide initial estimations of their usefulness by altering different parameters such as the group size and overlap percentage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1301.3533v2-abstract-full').style.display = 'none'; document.getElementById('1301.3533v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 February, 2013; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 January, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 7 figures (including subfigures), ICleaR conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0911.1305">arXiv:0911.1305</a> <span> [<a href="https://arxiv.org/pdf/0911.1305">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Digital Libraries">cs.DL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> Retrieval of very large numbers of items in the Web of Science: an exercise to develop accurate search strategies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arencibia-Jorge%2C+R">Ricardo Arencibia-Jorge</a>, <a href="/search/cs?searchtype=author&query=Leydesdorff%2C+L">Loet Leydesdorff</a>, <a href="/search/cs?searchtype=author&query=Chinchilla-Rodriguez%2C+Z">Zaida Chinchilla-Rodriguez</a>, <a href="/search/cs?searchtype=author&query=Rousseau%2C+R">Ronald Rousseau</a>, <a href="/search/cs?searchtype=author&query=Paris%2C+S+W">Soren W. Paris</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="0911.1305v1-abstract-short" style="display: inline;"> The current communication presents a simple exercise with the aim of solving a singular problem: the retrieval of extremely large amounts of items in the Web of Science interface. As it is known, Web of Science interface allows a user to obtain at most 100,000 items from a single query. But what about queries that achieve a result of more than 100,000 items? The exercise developed one possible w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0911.1305v1-abstract-full').style.display = 'inline'; document.getElementById('0911.1305v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0911.1305v1-abstract-full" style="display: none;"> The current communication presents a simple exercise with the aim of solving a singular problem: the retrieval of extremely large amounts of items in the Web of Science interface. As it is known, Web of Science interface allows a user to obtain at most 100,000 items from a single query. But what about queries that achieve a result of more than 100,000 items? The exercise developed one possible way to achieve this objective. The case study is the retrieval of the entire scientific production from the United States in a specific year. Different sections of items were retrieved using the field Source of the database. Then, a simple Boolean statement was created with the aim of eliminating overlapping and to improve the accuracy of the search strategy. The importance of team work in the development of advanced search strategies was noted. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0911.1305v1-abstract-full').style.display = 'none'; document.getElementById('0911.1305v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2009. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> El Profesional de la Informacion 18(5) (2009) 555-559 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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