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<button class="button is-small 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/2503.13358">arXiv:2503.13358</a> <span> [<a href="https://arxiv.org/pdf/2503.13358">pdf</a>, <a href="https://arxiv.org/format/2503.13358">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"> One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Selikhanovych%2C+D">Daniil Selikhanovych</a>, <a href="/search/cs?searchtype=author&query=Li%2C+D">David Li</a>, <a href="/search/cs?searchtype=author&query=Leonov%2C+A">Aleksei Leonov</a>, <a href="/search/cs?searchtype=author&query=Gushchin%2C+N">Nikita Gushchin</a>, <a href="/search/cs?searchtype=author&query=Kushneriuk%2C+S">Sergei Kushneriuk</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</a>, <a href="/search/cs?searchtype=author&query=Koshelev%2C+I">Iaroslav Koshelev</a>, <a href="/search/cs?searchtype=author&query=Korotin%2C+A">Alexander Korotin</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="2503.13358v1-abstract-short" style="display: inline;"> Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate diffusion-based SR models, some (e.g., SinSR) fail to produce realistic perceptual details, while others (e.g., OSEDiff) may hallucinate non-existent structures. To overcome these issues, we present RSD, a new distillatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13358v1-abstract-full').style.display = 'inline'; document.getElementById('2503.13358v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.13358v1-abstract-full" style="display: none;"> Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate diffusion-based SR models, some (e.g., SinSR) fail to produce realistic perceptual details, while others (e.g., OSEDiff) may hallucinate non-existent structures. To overcome these issues, we present RSD, a new distillation method for ResShift, one of the top diffusion-based SR models. Our method is based on training the student network to produce such images that a new fake ResShift model trained on them will coincide with the teacher model. RSD achieves single-step restoration and outperforms the teacher by a large margin. We show that our distillation method can surpass the other distillation-based method for ResShift - SinSR - making it on par with state-of-the-art diffusion-based SR distillation methods. Compared to SR methods based on pre-trained text-to-image models, RSD produces competitive perceptual quality, provides images with better alignment to degraded input images, and requires fewer parameters and GPU memory. We provide experimental results on various real-world and synthetic datasets, including RealSR, RealSet65, DRealSR, ImageNet, and DIV2K. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13358v1-abstract-full').style.display = 'none'; document.getElementById('2503.13358v1-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 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.15020">arXiv:2406.15020</a> <span> [<a href="https://arxiv.org/pdf/2406.15020">pdf</a>, <a href="https://arxiv.org/format/2406.15020">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"> A3D: Does Diffusion Dream about 3D Alignment? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ignatyev%2C+S">Savva Ignatyev</a>, <a href="/search/cs?searchtype=author&query=Konovalova%2C+N">Nina Konovalova</a>, <a href="/search/cs?searchtype=author&query=Selikhanovych%2C+D">Daniil Selikhanovych</a>, <a href="/search/cs?searchtype=author&query=Voynov%2C+O">Oleg Voynov</a>, <a href="/search/cs?searchtype=author&query=Patakin%2C+N">Nikolay Patakin</a>, <a href="/search/cs?searchtype=author&query=Olkov%2C+I">Ilya Olkov</a>, <a href="/search/cs?searchtype=author&query=Senushkin%2C+D">Dmitry Senushkin</a>, <a href="/search/cs?searchtype=author&query=Artemov%2C+A">Alexey Artemov</a>, <a href="/search/cs?searchtype=author&query=Konushin%2C+A">Anton Konushin</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Wonka%2C+P">Peter Wonka</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2406.15020v4-abstract-short" style="display: inline;"> We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically corresponding parts aligned across them. Recent methods based on Score Distillation have succeeded in distilling the knowledge from 2D diffusion models to high-quality representations of the 3D objects. These methods han… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.15020v4-abstract-full').style.display = 'inline'; document.getElementById('2406.15020v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.15020v4-abstract-full" style="display: none;"> We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically corresponding parts aligned across them. Recent methods based on Score Distillation have succeeded in distilling the knowledge from 2D diffusion models to high-quality representations of the 3D objects. These methods handle multiple text queries separately, and therefore the resulting objects have a high variability in object pose and structure. However, in some applications, such as 3D asset design, it may be desirable to obtain a set of objects aligned with each other. In order to achieve the alignment of the corresponding parts of the generated objects, we propose to embed these objects into a common latent space and optimize the continuous transitions between these objects. We enforce two kinds of properties of these transitions: smoothness of the transition and plausibility of the intermediate objects along the transition. We demonstrate that both of these properties are essential for good alignment. We provide several practical scenarios that benefit from alignment between the objects, including 3D editing and object hybridization, and experimentally demonstrate the effectiveness of our method. https://voyleg.github.io/a3d/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.15020v4-abstract-full').style.display = 'none'; document.getElementById('2406.15020v4-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> 16 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.01116">arXiv:2202.01116</a> <span> [<a href="https://arxiv.org/pdf/2202.01116">pdf</a>, <a href="https://arxiv.org/format/2202.01116">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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"> An Optimal Transport Perspective on Unpaired Image Super-Resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gazdieva%2C+M">Milena Gazdieva</a>, <a href="/search/cs?searchtype=author&query=Rout%2C+L">Litu Rout</a>, <a href="/search/cs?searchtype=author&query=Korotin%2C+A">Alexander Korotin</a>, <a href="/search/cs?searchtype=author&query=Kravchenko%2C+A">Andrey Kravchenko</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2202.01116v2-abstract-short" style="display: inline;"> Real-world image super-resolution (SR) tasks often do not have paired datasets, which limits the application of supervised techniques. As a result, the tasks are usually approached by unpaired techniques based on Generative Adversarial Networks (GANs), which yield complex training losses with several regularization terms, e.g., content or identity losses. We theoretically investigate optimization… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01116v2-abstract-full').style.display = 'inline'; document.getElementById('2202.01116v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.01116v2-abstract-full" style="display: none;"> Real-world image super-resolution (SR) tasks often do not have paired datasets, which limits the application of supervised techniques. As a result, the tasks are usually approached by unpaired techniques based on Generative Adversarial Networks (GANs), which yield complex training losses with several regularization terms, e.g., content or identity losses. We theoretically investigate optimization problems which arise in such models and find two surprizing observations. First, the learned SR map is always an optimal transport (OT) map. Second, we theoretically prove and empirically show that the learned map is biased, i.e., it does not actually transform the distribution of low-resolution images to high-resolution ones. Inspired by these findings, we propose an algorithm for unpaired SR which learns an unbiased OT map for the perceptual transport cost. Unlike the existing GAN-based alternatives, our algorithm has a simple optimization objective reducing the need for complex hyperparameter selection and an application of additional regularizations. At the same time, it provides a nearly state-of-the-art performance on the large-scale unpaired AIM19 dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01116v2-abstract-full').style.display = 'none'; document.getElementById('2202.01116v2-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> 11 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.05280">arXiv:2112.05280</a> <span> [<a href="https://arxiv.org/pdf/2112.05280">pdf</a>, <a href="https://arxiv.org/format/2112.05280">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Long-Range Thermal 3D Perception in Low Contrast Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Andrey Filippov</a>, <a href="/search/cs?searchtype=author&query=Filippova%2C+O">Olga Filippova</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="2112.05280v1-abstract-short" style="display: inline;"> This report discusses the results of SBIR Phase I effort to prove the feasibility of dramatic improvement of the microbolometer-based Long Wave Infrared (LWIR) detectors sensitivity, especially for the 3D measurements. The resulting low SWaP-C thermal depth-sensing system will enable the situational awareness of Autonomous Air Vehicles for Advanced Air Mobility (AAM). It will provide robust 3D inf… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.05280v1-abstract-full').style.display = 'inline'; document.getElementById('2112.05280v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.05280v1-abstract-full" style="display: none;"> This report discusses the results of SBIR Phase I effort to prove the feasibility of dramatic improvement of the microbolometer-based Long Wave Infrared (LWIR) detectors sensitivity, especially for the 3D measurements. The resulting low SWaP-C thermal depth-sensing system will enable the situational awareness of Autonomous Air Vehicles for Advanced Air Mobility (AAM). It will provide robust 3D information of the surrounding environment, including low-contrast static and moving objects, at far distances in degraded visual conditions and GPS-denied areas. Our multi-sensor 3D perception enabled by COTS uncooled thermal sensors mitigates major weakness of LWIR sensors - low contrast by increasing the system sensitivity over an order of magnitude. There were no available thermal image sets suitable for evaluating this technology, making datasets acquisition our first goal. We discuss the design and construction of the prototype system with sixteen 640pix x 512pix LWIR detectors, camera calibration to subpixel resolution, capture, and process synchronized image. The results show the 3.84x contrast increase for intrascene-only data and an additional 5.5x - with the interscene accumulation, reaching system noise-equivalent temperature difference (NETD) of 1.9 mK with the 40 mK sensors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.05280v1-abstract-full').style.display = 'none'; document.getElementById('2112.05280v1-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">13 pages, 16 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.04024">arXiv:2106.04024</a> <span> [<a href="https://arxiv.org/pdf/2106.04024">pdf</a>, <a href="https://arxiv.org/format/2106.04024">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="Artificial Intelligence">cs.AI</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="Algebraic Topology">math.AT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Metric Geometry">math.MG</span> </div> </div> <p class="title is-5 mathjax"> Manifold Topology Divergence: a Framework for Comparing Data Manifolds </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barannikov%2C+S">Serguei Barannikov</a>, <a href="/search/cs?searchtype=author&query=Trofimov%2C+I">Ilya Trofimov</a>, <a href="/search/cs?searchtype=author&query=Sotnikov%2C+G">Grigorii Sotnikov</a>, <a href="/search/cs?searchtype=author&query=Trimbach%2C+E">Ekaterina Trimbach</a>, <a href="/search/cs?searchtype=author&query=Korotin%2C+A">Alexander Korotin</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2106.04024v2-abstract-short" style="display: inline;"> We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifolds on which the distributions are concentrated. Based on the Cross-Barcode, we introduce the Manifol… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04024v2-abstract-full').style.display = 'inline'; document.getElementById('2106.04024v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.04024v2-abstract-full" style="display: none;"> We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifolds on which the distributions are concentrated. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it to assess the performance of deep generative models in various domains: images, 3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN, CIFAR10, FFHQ, chest X-ray images, market stock data, ShapeNet. We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance. Our algorithm scales well (essentially linearly) with the increase of the dimension of the ambient high-dimensional space. It is one of the first TDA-based practical methodologies that can be applied universally to datasets of different sizes and dimensions, including the ones on which the most recent GANs in the visual domain are trained. The proposed method is domain agnostic and does not rely on pre-trained networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04024v2-abstract-full').style.display = 'none'; document.getElementById('2106.04024v2-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 55N31; 68T07 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 35th Conference on Neural Information Processing Systems (NeurIPS 2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.01954">arXiv:2106.01954</a> <span> [<a href="https://arxiv.org/pdf/2106.01954">pdf</a>, <a href="https://arxiv.org/format/2106.01954">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> </div> </div> <p class="title is-5 mathjax"> Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korotin%2C+A">Alexander Korotin</a>, <a href="/search/cs?searchtype=author&query=Li%2C+L">Lingxiao Li</a>, <a href="/search/cs?searchtype=author&query=Genevay%2C+A">Aude Genevay</a>, <a href="/search/cs?searchtype=author&query=Solomon%2C+J">Justin Solomon</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2106.01954v2-abstract-short" style="display: inline;"> Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance. In this paper, we address this issue for quadratic-cost transport -- specifically, computation of the Wasserstein-2 distance, a commonly-used formulation of optimal transport in machine learning. To overcome the challenge of computing ground… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.01954v2-abstract-full').style.display = 'inline'; document.getElementById('2106.01954v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.01954v2-abstract-full" style="display: none;"> Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance. In this paper, we address this issue for quadratic-cost transport -- specifically, computation of the Wasserstein-2 distance, a commonly-used formulation of optimal transport in machine learning. To overcome the challenge of computing ground truth transport maps between continuous measures needed to assess these solvers, we use input-convex neural networks (ICNN) to construct pairs of measures whose ground truth OT maps can be obtained analytically. This strategy yields pairs of continuous benchmark measures in high-dimensional spaces such as spaces of images. We thoroughly evaluate existing optimal transport solvers using these benchmark measures. Even though these solvers perform well in downstream tasks, many do not faithfully recover optimal transport maps. To investigate the cause of this discrepancy, we further test the solvers in a setting of image generation. Our study reveals crucial limitations of existing solvers and shows that increased OT accuracy does not necessarily correlate to better results downstream. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.01954v2-abstract-full').style.display = 'none'; document.getElementById('2106.01954v2-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.12038">arXiv:2105.12038</a> <span> [<a href="https://arxiv.org/pdf/2105.12038">pdf</a>, <a href="https://arxiv.org/format/2105.12038">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"> Unpaired Depth Super-Resolution in the Wild </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Safin%2C+A">Aleksandr Safin</a>, <a href="/search/cs?searchtype=author&query=Kan%2C+M">Maxim Kan</a>, <a href="/search/cs?searchtype=author&query=Drobyshev%2C+N">Nikita Drobyshev</a>, <a href="/search/cs?searchtype=author&query=Voynov%2C+O">Oleg Voynov</a>, <a href="/search/cs?searchtype=author&query=Artemov%2C+A">Alexey Artemov</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Zorin%2C+D">Denis Zorin</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2105.12038v4-abstract-short" style="display: inline;"> Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-world paired data requires specialized setups. Another alternative, generating lo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.12038v4-abstract-full').style.display = 'inline'; document.getElementById('2105.12038v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.12038v4-abstract-full" style="display: none;"> Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-world paired data requires specialized setups. Another alternative, generating low-resolution maps from high-resolution maps by subsampling, adding noise and other artificial degradation methods, does not fully capture the characteristics of real-world low-resolution images. As a consequence, supervised learning methods trained on such artificial paired data may not perform well on real-world low-resolution inputs. We consider an approach to depth super-resolution based on learning from unpaired data. While many techniques for unpaired image-to-image translation have been proposed, most fail to deliver effective hole-filling or reconstruct accurate surfaces using depth maps. We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps. We propose a benchmark for unpaired depth SR and demonstrate that our method outperforms existing unpaired methods and performs on par with paired. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.12038v4-abstract-full').style.display = 'none'; document.getElementById('2105.12038v4-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> 23 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.08341">arXiv:2006.08341</a> <span> [<a href="https://arxiv.org/pdf/2006.08341">pdf</a>, <a href="https://arxiv.org/format/2006.08341">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"> Multi-fidelity Neural Architecture Search with Knowledge Distillation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Trofimov%2C+I">Ilya Trofimov</a>, <a href="/search/cs?searchtype=author&query=Klyuchnikov%2C+N">Nikita Klyuchnikov</a>, <a href="/search/cs?searchtype=author&query=Salnikov%2C+M">Mikhail Salnikov</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Alexander Filippov</a>, <a href="/search/cs?searchtype=author&query=Burnaev%2C+E">Evgeny Burnaev</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="2006.08341v2-abstract-short" style="display: inline;"> Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very time-consuming. One of the possible ways to mitigate this issue is to use low-fidelity evaluations, namely training on a part of a dataset, fewer epochs, with fewer channels, etc. In this paper, we propose a bayesian mu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.08341v2-abstract-full').style.display = 'inline'; document.getElementById('2006.08341v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.08341v2-abstract-full" style="display: none;"> Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very time-consuming. One of the possible ways to mitigate this issue is to use low-fidelity evaluations, namely training on a part of a dataset, fewer epochs, with fewer channels, etc. In this paper, we propose a bayesian multi-fidelity method for neural architecture search: MF-KD. The method relies on a new approach to low-fidelity evaluations of neural architectures by training for a few epochs using a knowledge distillation. Knowledge distillation adds to a loss function a term forcing a network to mimic some teacher network. We carry out experiments on CIFAR-10, CIFAR-100, and ImageNet-16-120. We show that training for a few epochs with such a modified loss function leads to a better selection of neural architectures than training for a few epochs with a logistic loss. The proposed method outperforms several state-of-the-art baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.08341v2-abstract-full').style.display = 'none'; document.getElementById('2006.08341v2-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 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 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/1911.06975">arXiv:1911.06975</a> <span> [<a href="https://arxiv.org/pdf/1911.06975">pdf</a>, <a href="https://arxiv.org/format/1911.06975">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"> Long Range 3D with Quadocular Thermal (LWIR) Camera </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Andrey Filippov</a>, <a href="/search/cs?searchtype=author&query=Dzhimiev%2C+O">Oleg Dzhimiev</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="1911.06975v2-abstract-short" style="display: inline;"> Long Wave Infrared (LWIR) cameras provide images regardles of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible range ones, including 3D scene reconstruction with two or more such cameras mounted… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06975v2-abstract-full').style.display = 'inline'; document.getElementById('1911.06975v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.06975v2-abstract-full" style="display: none;"> Long Wave Infrared (LWIR) cameras provide images regardles of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible range ones, including 3D scene reconstruction with two or more such cameras mounted on a rigid frame. There are two additional challenges for this spectral range: lower image resolution and lower contrast of the textures. In this work, we demonstrate quadocular LWIR camera setup, calibration, image capturing and processing that result in long range 3D perception with 0.077 pix disparity error over 90% of the depth map. With low resolution (160 x 120) LWIR sensors we achieved 10% range accuracy at 28 m with 56 degrees horizontal field of view (HFoV) and 150 mm baseline. Scaled to the now-standard 640 x 512 resolution and 200 mm baseline suitable for head-mounted application the result would be 10% accuracy at 130 m. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06975v2-abstract-full').style.display = 'none'; document.getElementById('1911.06975v2-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </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, 5 figures; fixed abbreviations navigation, added pdf ToC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.08032">arXiv:1811.08032</a> <span> [<a href="https://arxiv.org/pdf/1811.08032">pdf</a>, <a href="https://arxiv.org/format/1811.08032">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"> See far with TPNET: a Tile Processor and a CNN Symbiosis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Filippov%2C+A">Andrey Filippov</a>, <a href="/search/cs?searchtype=author&query=Dzhimiev%2C+O">Oleg Dzhimiev</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="1811.08032v1-abstract-short" style="display: inline;"> Throughout the evolution of the neural networks more specialized cells were added to the set of basic building blocks. These cells aim to improve training convergence, increase the overall performance, and reduce the number of required labels, all while preserving the expressive power of the universal network. Inspired by the partitioning of the human visual perception system between the eyes and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.08032v1-abstract-full').style.display = 'inline'; document.getElementById('1811.08032v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.08032v1-abstract-full" style="display: none;"> Throughout the evolution of the neural networks more specialized cells were added to the set of basic building blocks. These cells aim to improve training convergence, increase the overall performance, and reduce the number of required labels, all while preserving the expressive power of the universal network. Inspired by the partitioning of the human visual perception system between the eyes and the cerebral cortex, we present TPNET, which offloads universal and application-specific CNN from the bulk processing of the high resolution pixel data and performs the translation-variant image correction while delegating all non-linear decision making to the network. In this work, we explore application of TPNET to 3D perception with a narrow-baseline (0.0001-0.0025) quad stereo camera and prove that a trained network provides a disparity prediction from the 2D phase correlation output by the Tile Processor (TP) that is twice as accurate as the prediction from a carefully hand-crafted algorithm. The TP in turn reduces the dimensions of the input features of the network and provides instrument-invariant and translation-invariant data, making real-time high resolution stereo 3D perception feasible and easing the requirement to have a complete end-to-end network. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.08032v1-abstract-full').style.display = 'none'; document.getElementById('1811.08032v1-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 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </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, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.06595">arXiv:1701.06595</a> <span> [<a href="https://arxiv.org/pdf/1701.06595">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey 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/10.15514/ISPRAS-2016-28(6)-10">10.15514/ISPRAS-2016-28(6)-10 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ignatov%2C+D+Y">Dmitry Yu. Ignatov</a>, <a href="/search/cs?searchtype=author&query=Filippov%2C+A+N">Alexander N. Filippov</a>, <a href="/search/cs?searchtype=author&query=Ignatov%2C+A+D">Andrey D. Ignatov</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xuecang Zhang</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="1701.06595v1-abstract-short" style="display: inline;"> The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous functioning requires automatic screening and permanent optimization with processing of the huge amount of data by high-performance distributed systems. We propose new m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.06595v1-abstract-full').style.display = 'inline'; document.getElementById('1701.06595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.06595v1-abstract-full" style="display: none;"> The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous functioning requires automatic screening and permanent optimization with processing of the huge amount of data by high-performance distributed systems. We propose new meta-algorithm of large homogeneous network analysis, its decomposition into alternative sets of loosely connected subnets, and parallel optimization of the most independent elements. This algorithm is based on a network-specific correlation function, Simulated Annealing technique, and is adapted to work in the computer cluster. On the example of large wireless network, we show that proposed algorithm essentially increases speed of parallel optimization. The elaborated general approach can be used for analysis and optimization of the wide range of networks, including such specific types as artificial neural networks or organized in networks physiological systems of living organisms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.06595v1-abstract-full').style.display = 'none'; document.getElementById('1701.06595v1-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> 23 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">Article is published in "Proceedings of ISP RAS" under Creative Commons Attribution (CC BY 4.0) license - https://creativecommons.org/licenses/by/4.0/ Original copy of article is uploaded</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Trudy ISP RAN/Proc. ISP RAS, vol. 28, issue 6, 2016, pp. 141-152 </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" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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