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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/2406.03359">arXiv:2406.03359</a> <span> [<a href="https://arxiv.org/pdf/2406.03359">pdf</a>, <a href="https://arxiv.org/format/2406.03359">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> </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/10.1007/978-3-031-16980-9_13">10.1007/978-3-031-16980-9_13 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Forigua%2C+C">Cristhian Forigua</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Maria Escobar</a>, <a href="/search/cs?searchtype=author&query=Arbelaez%2C+P">Pablo Arbelaez</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="2406.03359v1-abstract-short" style="display: inline;"> This paper presents a novel framework for processing volumetric medical information using Visual Transformers (ViTs). First, We extend the state-of-the-art Swin Transformer model to the 3D medical domain. Second, we propose a new approach for processing volumetric information and encoding position in ViTs for 3D applications. We instantiate the proposed framework and present SuperFormer, a volumet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03359v1-abstract-full').style.display = 'inline'; document.getElementById('2406.03359v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.03359v1-abstract-full" style="display: none;"> This paper presents a novel framework for processing volumetric medical information using Visual Transformers (ViTs). First, We extend the state-of-the-art Swin Transformer model to the 3D medical domain. Second, we propose a new approach for processing volumetric information and encoding position in ViTs for 3D applications. We instantiate the proposed framework and present SuperFormer, a volumetric transformer-based approach for Magnetic Resonance Imaging (MRI) Super-Resolution. Our method leverages the 3D information of the MRI domain and uses a local self-attention mechanism with a 3D relative positional encoding to recover anatomical details. In addition, our approach takes advantage of multi-domain information from volume and feature domains and fuses them to reconstruct the High-Resolution MRI. We perform an extensive validation on the Human Connectome Project dataset and demonstrate the superiority of volumetric transformers over 3D CNN-based methods. Our code and pretrained models are available at https://github.com/BCV-Uniandes/SuperFormer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03359v1-abstract-full').style.display = 'none'; document.getElementById('2406.03359v1-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> 5 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.18259">arXiv:2311.18259</a> <span> [<a href="https://arxiv.org/pdf/2311.18259">pdf</a>, <a href="https://arxiv.org/format/2311.18259">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Grauman%2C+K">Kristen Grauman</a>, <a href="/search/cs?searchtype=author&query=Westbury%2C+A">Andrew Westbury</a>, <a href="/search/cs?searchtype=author&query=Torresani%2C+L">Lorenzo Torresani</a>, <a href="/search/cs?searchtype=author&query=Kitani%2C+K">Kris Kitani</a>, <a href="/search/cs?searchtype=author&query=Malik%2C+J">Jitendra Malik</a>, <a href="/search/cs?searchtype=author&query=Afouras%2C+T">Triantafyllos Afouras</a>, <a href="/search/cs?searchtype=author&query=Ashutosh%2C+K">Kumar Ashutosh</a>, <a href="/search/cs?searchtype=author&query=Baiyya%2C+V">Vijay Baiyya</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+S">Siddhant Bansal</a>, <a href="/search/cs?searchtype=author&query=Boote%2C+B">Bikram Boote</a>, <a href="/search/cs?searchtype=author&query=Byrne%2C+E">Eugene Byrne</a>, <a href="/search/cs?searchtype=author&query=Chavis%2C+Z">Zach Chavis</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Joya Chen</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+F">Feng Cheng</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+F">Fu-Jen Chu</a>, <a href="/search/cs?searchtype=author&query=Crane%2C+S">Sean Crane</a>, <a href="/search/cs?searchtype=author&query=Dasgupta%2C+A">Avijit Dasgupta</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+J">Jing Dong</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Maria Escobar</a>, <a href="/search/cs?searchtype=author&query=Forigua%2C+C">Cristhian Forigua</a>, <a href="/search/cs?searchtype=author&query=Gebreselasie%2C+A">Abrham Gebreselasie</a>, <a href="/search/cs?searchtype=author&query=Haresh%2C+S">Sanjay Haresh</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+J">Jing Huang</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+M">Md Mohaiminul Islam</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S">Suyog Jain</a> , et al. (76 additional authors not shown) </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="2311.18259v4-abstract-short" style="display: inline;"> We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18259v4-abstract-full').style.display = 'inline'; document.getElementById('2311.18259v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.18259v4-abstract-full" style="display: none;"> We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions -- including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community. Project page: http://ego-exo4d-data.org/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18259v4-abstract-full').style.display = 'none'; document.getElementById('2311.18259v4-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Expanded manuscript (compared to arxiv v1 from Nov 2023 and CVPR 2024 paper from June 2024) for more comprehensive dataset and benchmark presentation, plus new results on v2 data release</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.16606">arXiv:2306.16606</a> <span> [<a href="https://arxiv.org/pdf/2306.16606">pdf</a>, <a href="https://arxiv.org/format/2306.16606">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"> EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Forigua%2C+C">Cristhian Forigua</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Maria Escobar</a>, <a href="/search/cs?searchtype=author&query=Pont-Tuset%2C+J">Jordi Pont-Tuset</a>, <a href="/search/cs?searchtype=author&query=Maninis%2C+K">Kevis-Kokitsi Maninis</a>, <a href="/search/cs?searchtype=author&query=Arbel%C3%A1ez%2C+P">Pablo Arbel谩ez</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="2306.16606v1-abstract-short" style="display: inline;"> We present EgoCOL, an egocentric camera pose estimation method for open-world 3D object localization. Our method leverages sparse camera pose reconstructions in a two-fold manner, video and scan independently, to estimate the camera pose of egocentric frames in 3D renders with high recall and precision. We extensively evaluate our method on the Visual Query (VQ) 3D object localization Ego4D benchm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16606v1-abstract-full').style.display = 'inline'; document.getElementById('2306.16606v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.16606v1-abstract-full" style="display: none;"> We present EgoCOL, an egocentric camera pose estimation method for open-world 3D object localization. Our method leverages sparse camera pose reconstructions in a two-fold manner, video and scan independently, to estimate the camera pose of egocentric frames in 3D renders with high recall and precision. We extensively evaluate our method on the Visual Query (VQ) 3D object localization Ego4D benchmark. EgoCOL can estimate 62% and 59% more camera poses than the Ego4D baseline in the Ego4D Visual Queries 3D Localization challenge at CVPR 2023 in the val and test sets, respectively. Our code is publicly available at https://github.com/BCV-Uniandes/EgoCOL <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16606v1-abstract-full').style.display = 'none'; document.getElementById('2306.16606v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.08787">arXiv:2305.08787</a> <span> [<a href="https://arxiv.org/pdf/2305.08787">pdf</a>, <a href="https://arxiv.org/format/2305.08787">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Comparing Variation in Tokenizer Outputs Using a Series of Problematic and Challenging Biomedical Sentences </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Meaney%2C+C">Christopher Meaney</a>, <a href="/search/cs?searchtype=author&query=Stukel%2C+T+A">Therese A Stukel</a>, <a href="/search/cs?searchtype=author&query=Austin%2C+P+C">Peter C Austin</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Michael Escobar</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="2305.08787v1-abstract-short" style="display: inline;"> Background & Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic meaning. The objective of this study is to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08787v1-abstract-full').style.display = 'inline'; document.getElementById('2305.08787v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.08787v1-abstract-full" style="display: none;"> Background & Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic meaning. The objective of this study is to explore variation in tokenizer outputs when applied across a series of challenging biomedical sentences. Method: Diaz [2015] introduce 24 challenging example biomedical sentences for comparing tokenizer performance. In this study, we descriptively explore variation in outputs of eight tokenizers applied to each example biomedical sentence. The tokenizers compared in this study are the NLTK white space tokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers, Stanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers. Results: For many examples, tokenizers performed similarly effectively; however, for certain examples, there were meaningful variation in returned outputs. The white space tokenizer often performed differently than other tokenizers. We observed performance similarities for tokenizers implementing rule-based systems (e.g. pattern matching and regular expressions) and tokenizers implementing neural architectures for token classification. Oftentimes, the challenging tokens resulting in the greatest variation in outputs, are those words which convey substantive and focused biomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and (Ca2+)-regulated). Conclusion: When state-of-the-art, open-source tokenizers from Python and R were applied to a series of challenging biomedical example sentences, we observed subtle variation in the returned outputs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08787v1-abstract-full').style.display = 'none'; document.getElementById('2305.08787v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.11118">arXiv:2304.11118</a> <span> [<a href="https://arxiv.org/pdf/2304.11118">pdf</a>, <a href="https://arxiv.org/format/2304.11118">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Castillo%2C+A">Angela Castillo</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Maria Escobar</a>, <a href="/search/cs?searchtype=author&query=Jeanneret%2C+G">Guillaume Jeanneret</a>, <a href="/search/cs?searchtype=author&query=Pumarola%2C+A">Albert Pumarola</a>, <a href="/search/cs?searchtype=author&query=Arbel%C3%A1ez%2C+P">Pablo Arbel谩ez</a>, <a href="/search/cs?searchtype=author&query=Thabet%2C+A">Ali Thabet</a>, <a href="/search/cs?searchtype=author&query=Sanakoyeu%2C+A">Artsiom Sanakoyeu</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="2304.11118v1-abstract-short" style="display: inline;"> Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand movements, leading to a limited reconstruction of full-body motion due to variability in lower body configurations. We propose BoDiffusion -- a generative diffusion model for motion synthesis to tackle this under-constrained r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11118v1-abstract-full').style.display = 'inline'; document.getElementById('2304.11118v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.11118v1-abstract-full" style="display: none;"> Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand movements, leading to a limited reconstruction of full-body motion due to variability in lower body configurations. We propose BoDiffusion -- a generative diffusion model for motion synthesis to tackle this under-constrained reconstruction problem. We present a time and space conditioning scheme that allows BoDiffusion to leverage sparse tracking inputs while generating smooth and realistic full-body motion sequences. To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task. We conduct experiments on the large-scale motion-capture dataset AMASS and show that our approach outperforms the state-of-the-art approaches by a significant margin in terms of full-body motion realism and joint reconstruction error. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11118v1-abstract-full').style.display = 'none'; document.getElementById('2304.11118v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.11329">arXiv:2207.11329</a> <span> [<a href="https://arxiv.org/pdf/2207.11329">pdf</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"> Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Maria Escobar</a>, <a href="/search/cs?searchtype=author&query=Daza%2C+L">Laura Daza</a>, <a href="/search/cs?searchtype=author&query=Gonz%C3%A1lez%2C+C">Cristina Gonz谩lez</a>, <a href="/search/cs?searchtype=author&query=Pont-Tuset%2C+J">Jordi Pont-Tuset</a>, <a href="/search/cs?searchtype=author&query=Arbel%C3%A1ez%2C+P">Pablo Arbel谩ez</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="2207.11329v1-abstract-short" style="display: inline;"> We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification. Our method achieved competitive performance on both challenges. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.11329v1-abstract-full" style="display: none;"> We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification. Our method achieved competitive performance on both challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11329v1-abstract-full').style.display = 'none'; document.getElementById('2207.11329v1-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.11505">arXiv:2108.11505</a> <span> [<a href="https://arxiv.org/pdf/2108.11505">pdf</a>, <a href="https://arxiv.org/format/2108.11505">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"> Generalized Real-World Super-Resolution through Adversarial Robustness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Castillo%2C+A">Angela Castillo</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Mar铆a Escobar</a>, <a href="/search/cs?searchtype=author&query=P%C3%A9rez%2C+J+C">Juan C. P茅rez</a>, <a href="/search/cs?searchtype=author&query=Romero%2C+A">Andr茅s Romero</a>, <a href="/search/cs?searchtype=author&query=Timofte%2C+R">Radu Timofte</a>, <a href="/search/cs?searchtype=author&query=Van+Gool%2C+L">Luc Van Gool</a>, <a href="/search/cs?searchtype=author&query=Arbel%C3%A1ez%2C+P">Pablo Arbel谩ez</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="2108.11505v1-abstract-short" style="display: inline;"> Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution imagery. Thus, current methods lack generalization and lose their accuracy when tested on unseen types of corruption. In contrast to the traditional proposal, we present Robust Super-Resolution (RSR), a method that levera… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11505v1-abstract-full').style.display = 'inline'; document.getElementById('2108.11505v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.11505v1-abstract-full" style="display: none;"> Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution imagery. Thus, current methods lack generalization and lose their accuracy when tested on unseen types of corruption. In contrast to the traditional proposal, we present Robust Super-Resolution (RSR), a method that leverages the generalization capability of adversarial attacks to tackle real-world SR. Our novel framework poses a paradigm shift in the development of real-world SR methods. Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses. Afterward, we use these adversarial examples during training to improve our model's capacity to process noisy inputs. We perform extensive experimentation on synthetic and real-world images and empirically demonstrate that our RSR method generalizes well across datasets without re-training for specific noise priors. By using a single robust model, we outperform state-of-the-art specialized methods on real-world benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11505v1-abstract-full').style.display = 'none'; document.getElementById('2108.11505v1-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 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">ICCV Workshops, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.05454">arXiv:2007.05454</a> <span> [<a href="https://arxiv.org/pdf/2007.05454">pdf</a>, <a href="https://arxiv.org/format/2007.05454">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> </div> </div> <p class="title is-5 mathjax"> SIMBA: Specific Identity Markers for Bone Age Assessment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gonz%C3%A1lez%2C+C">Cristina Gonz谩lez</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Mar铆a Escobar</a>, <a href="/search/cs?searchtype=author&query=Daza%2C+L">Laura Daza</a>, <a href="/search/cs?searchtype=author&query=Torres%2C+F">Felipe Torres</a>, <a href="/search/cs?searchtype=author&query=Triana%2C+G">Gustavo Triana</a>, <a href="/search/cs?searchtype=author&query=Arbel%C3%A1ez%2C+P">Pablo Arbel谩ez</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="2007.05454v2-abstract-short" style="display: inline;"> Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity markers when calculating bone age, i.e., chronological age and gender. However, the current automated Bone Age Assessment methods do not completely exploit the information present in the patient's metadata. With this lack of a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.05454v2-abstract-full').style.display = 'inline'; document.getElementById('2007.05454v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.05454v2-abstract-full" style="display: none;"> Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity markers when calculating bone age, i.e., chronological age and gender. However, the current automated Bone Age Assessment methods do not completely exploit the information present in the patient's metadata. With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment. SIMBA is a novel approach for the task of BAA based on the use of identity markers. For this purpose, we build upon the state-of-the-art model, fusing the information present in the identity markers with the visual features created from the original hand radiograph. We then use this robust representation to estimate the patient's relative bone age: the difference between chronological age and bone age. We validate SIMBA on the Radiological Hand Pose Estimation dataset and find that it outperforms previous state-of-the-art methods. SIMBA sets a trend of a new wave of Computer-aided Diagnosis methods that incorporate all of the data that is available regarding a patient. To promote further research in this area and ensure reproducibility we will provide the source code as well as the pre-trained models of SIMBA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.05454v2-abstract-full').style.display = 'none'; document.getElementById('2007.05454v2-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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 at MICCAI 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.09822">arXiv:2006.09822</a> <span> [<a href="https://arxiv.org/pdf/2006.09822">pdf</a>, <a href="https://arxiv.org/format/2006.09822">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Optimization and Control">math.OC</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/10.1080/00986445.2020.1771319">10.1080/00986445.2020.1771319 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Critical Point Calculations by Numerical Inversion of Functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Parajara%2C+C+N">C. N. Parajara</a>, <a href="/search/cs?searchtype=author&query=Platt%2C+G+M">G. M. Platt</a>, <a href="/search/cs?searchtype=author&query=Neto%2C+F+D+M">F. D. Moura Neto</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">M. Escobar</a>, <a href="/search/cs?searchtype=author&query=Libotte%2C+G+B">G. B. Libotte</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="2006.09822v1-abstract-short" style="display: inline;"> In this work, we propose a new approach to the problem of critical point calculation, based on the formulation of Heidemann and Khalil (1980). This leads to a $2 \times 2$ system of nonlinear algebraic equations in temperature and molar volume, which makes possible the prediction of critical points of the mixture through an adaptation of the technique of inversion of functions from the plane to th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.09822v1-abstract-full').style.display = 'inline'; document.getElementById('2006.09822v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.09822v1-abstract-full" style="display: none;"> In this work, we propose a new approach to the problem of critical point calculation, based on the formulation of Heidemann and Khalil (1980). This leads to a $2 \times 2$ system of nonlinear algebraic equations in temperature and molar volume, which makes possible the prediction of critical points of the mixture through an adaptation of the technique of inversion of functions from the plane to the plane, proposed by Malta, Saldanha, and Tomei (1993). The results are compared to those obtained by three methodologies: ($i$) the classical method of Heidemann and Khalil (1980), which uses a double-loop structure, also in terms of temperature and molar volume; ($ii$) the algorithm of Dimitrakopoulos, Jia, and Li (2014), which employs a damped Newton algorithm and ($iii$) the methodology proposed by Nichita and Gomez (2010), based on a stochastic algorithm. The proposed methodology proves to be robust and accurate in the prediction of critical points, as well as provides a global view of the nonlinear problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.09822v1-abstract-full').style.display = 'none'; document.getElementById('2006.09822v1-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 May, 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/2005.13156">arXiv:2005.13156</a> <span> [<a href="https://arxiv.org/pdf/2005.13156">pdf</a>, <a href="https://arxiv.org/format/2005.13156">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> MT-Adapted Datasheets for Datasets: Template and Repository </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Costa-juss%C3%A0%2C+M+R">Marta R. Costa-juss脿</a>, <a href="/search/cs?searchtype=author&query=Creus%2C+R">Roger Creus</a>, <a href="/search/cs?searchtype=author&query=Domingo%2C+O">Oriol Domingo</a>, <a href="/search/cs?searchtype=author&query=Dom%C3%ADnguez%2C+A">Albert Dom铆nguez</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Miquel Escobar</a>, <a href="/search/cs?searchtype=author&query=L%C3%B3pez%2C+C">Cayetana L贸pez</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+M">Marina Garcia</a>, <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</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="2005.13156v1-abstract-short" style="display: inline;"> In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019). Within this documentation process, we have adapted the original datasheet to the particular case of data consumers within the Machine Translation area. We are also proposing a reposito… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13156v1-abstract-full').style.display = 'inline'; document.getElementById('2005.13156v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.13156v1-abstract-full" style="display: none;"> In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019). Within this documentation process, we have adapted the original datasheet to the particular case of data consumers within the Machine Translation area. We are also proposing a repository for collecting the adapted datasheets in this research area <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13156v1-abstract-full').style.display = 'none'; document.getElementById('2005.13156v1-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 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">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1706.04449">arXiv:1706.04449</a> <span> [<a href="https://arxiv.org/pdf/1706.04449">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Classical Physics">physics.class-ph</span> </div> </div> <p class="title is-5 mathjax"> Damage detection in a unidimensional truss using the firefly optimization algorithm and finite elements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Escobar%2C+C+M">Camilo Manrique Escobar</a>, <a href="/search/cs?searchtype=author&query=Gonz%C3%A1lez-Estrada%2C+O+A">Octavio Andr茅s Gonz谩lez-Estrada</a>, <a href="/search/cs?searchtype=author&query=Acevedo%2C+H+G+S">Heller Guillermo S谩nchez Acevedo</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="1706.04449v2-abstract-short" style="display: inline;"> In this paper, we investigate the damage detection of structures seen as an optimization problem, using modal characterization to evaluate the dynamic response of the structure given a damage model. We implemented the firefly optimization algorithm with a simple numerical damage model to assess the performance of the method and its advantages for structural health monitoring (SHM). We show some im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.04449v2-abstract-full').style.display = 'inline'; document.getElementById('1706.04449v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.04449v2-abstract-full" style="display: none;"> In this paper, we investigate the damage detection of structures seen as an optimization problem, using modal characterization to evaluate the dynamic response of the structure given a damage model. We implemented the firefly optimization algorithm with a simple numerical damage model to assess the performance of the method and its advantages for structural health monitoring (SHM). We show some implementation details and discuss the obtained results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.04449v2-abstract-full').style.display = 'none'; document.getElementById('1706.04449v2-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 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">PREPRINT PAPER, submitted for publication</span> </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 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