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</div> <div class="control"> <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/2409.10587">arXiv:2409.10587</a> <span> [<a href="https://arxiv.org/pdf/2409.10587">pdf</a>, <a href="https://arxiv.org/format/2409.10587">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"> SoccerNet 2024 Challenges Results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cioppa%2C+A">Anthony Cioppa</a>, <a href="/search/cs?searchtype=author&query=Giancola%2C+S">Silvio Giancola</a>, <a href="/search/cs?searchtype=author&query=Somers%2C+V">Vladimir Somers</a>, <a href="/search/cs?searchtype=author&query=Joos%2C+V">Victor Joos</a>, <a href="/search/cs?searchtype=author&query=Magera%2C+F">Floriane Magera</a>, <a href="/search/cs?searchtype=author&query=Held%2C+J">Jan Held</a>, <a href="/search/cs?searchtype=author&query=Ghasemzadeh%2C+S+A">Seyed Abolfazl Ghasemzadeh</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xin Zhou</a>, <a href="/search/cs?searchtype=author&query=Seweryn%2C+K">Karolina Seweryn</a>, <a href="/search/cs?searchtype=author&query=Kowalczyk%2C+M">Mateusz Kowalczyk</a>, <a href="/search/cs?searchtype=author&query=Mr%C3%B3z%2C+Z">Zuzanna Mr贸z</a>, <a href="/search/cs?searchtype=author&query=%C5%81ukasik%2C+S">Szymon 艁ukasik</a>, <a href="/search/cs?searchtype=author&query=Ha%C5%82o%C5%84%2C+M">Micha艂 Ha艂o艅</a>, <a href="/search/cs?searchtype=author&query=Mkhallati%2C+H">Hassan Mkhallati</a>, <a href="/search/cs?searchtype=author&query=Deli%C3%A8ge%2C+A">Adrien Deli猫ge</a>, <a href="/search/cs?searchtype=author&query=Hinojosa%2C+C">Carlos Hinojosa</a>, <a href="/search/cs?searchtype=author&query=Sanchez%2C+K">Karen Sanchez</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Miralles%2C+P">Pierre Miralles</a>, <a href="/search/cs?searchtype=author&query=Barnich%2C+O">Olivier Barnich</a>, <a href="/search/cs?searchtype=author&query=De+Vleeschouwer%2C+C">Christophe De Vleeschouwer</a>, <a href="/search/cs?searchtype=author&query=Alahi%2C+A">Alexandre Alahi</a>, <a href="/search/cs?searchtype=author&query=Ghanem%2C+B">Bernard Ghanem</a>, <a href="/search/cs?searchtype=author&query=Van+Droogenbroeck%2C+M">Marc Van Droogenbroeck</a>, <a href="/search/cs?searchtype=author&query=Gorski%2C+A">Adam Gorski</a> , et al. (59 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="2409.10587v1-abstract-short" style="display: inline;"> The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely loca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10587v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10587v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10587v1-abstract-full" style="display: none;"> The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10587v1-abstract-full').style.display = 'none'; document.getElementById('2409.10587v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">7 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.16661">arXiv:2408.16661</a> <span> [<a href="https://arxiv.org/pdf/2408.16661">pdf</a>, <a href="https://arxiv.org/format/2408.16661">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"> Improving Weakly-supervised Video Instance Segmentation by Leveraging Spatio-temporal Consistency </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arefi%2C+F">Farnoosh Arefi</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2408.16661v2-abstract-short" style="display: inline;"> The performance of Video Instance Segmentation (VIS) methods has improved significantly with the advent of transformer networks. However, these networks often face challenges in training due to the high annotation cost. To address this, unsupervised and weakly-supervised methods have been developed to reduce the dependency on annotations. This work introduces a novel weakly-supervised method calle… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16661v2-abstract-full').style.display = 'inline'; document.getElementById('2408.16661v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.16661v2-abstract-full" style="display: none;"> The performance of Video Instance Segmentation (VIS) methods has improved significantly with the advent of transformer networks. However, these networks often face challenges in training due to the high annotation cost. To address this, unsupervised and weakly-supervised methods have been developed to reduce the dependency on annotations. This work introduces a novel weakly-supervised method called Eigen-Cluster VIS that, without requiring any mask annotations, achieves competitive accuracy compared to other VIS approaches. This method is based on two key innovations: a Temporal Eigenvalue Loss (TEL) and a clip-level Quality Cluster Coefficient (QCC). The TEL ensures temporal coherence by leveraging the eigenvalues of the Laplacian matrix derived from graph adjacency matrices. By minimizing the mean absolute error between the eigenvalues of adjacent frames, this loss function promotes smooth transitions and stable segmentation boundaries over time, reducing temporal discontinuities and improving overall segmentation quality. The QCC employs the K-means method to ensure the quality of spatio-temporal clusters without relying on ground truth masks. Using the Davies-Bouldin score, the QCC provides an unsupervised measure of feature discrimination, allowing the model to self-evaluate and adapt to varying object distributions, enhancing robustness during the testing phase. These enhancements are computationally efficient and straightforward, offering significant performance gains without additional annotated data. The proposed Eigen-Cluster VIS method is evaluated on the YouTube-Video Instance Segmentation (YouTube-VIS) 2019/2021 and Occluded Video Instance Segmentation (OVIS) datasets, demonstrating that it effectively narrows the performance gap between the fully-supervised and weakly-supervised VIS approaches. The code is available on https://github.com/farnooshar/EigenClusterVIS <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16661v2-abstract-full').style.display = 'none'; document.getElementById('2408.16661v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">12 pages, 6 Figures, 5 tabels</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.11335">arXiv:2404.11335</a> <span> [<a href="https://arxiv.org/pdf/2404.11335">pdf</a>, <a href="https://arxiv.org/format/2404.11335">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> <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"> SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Somers%2C+V">Vladimir Somers</a>, <a href="/search/cs?searchtype=author&query=Joos%2C+V">Victor Joos</a>, <a href="/search/cs?searchtype=author&query=Cioppa%2C+A">Anthony Cioppa</a>, <a href="/search/cs?searchtype=author&query=Giancola%2C+S">Silvio Giancola</a>, <a href="/search/cs?searchtype=author&query=Ghasemzadeh%2C+S+A">Seyed Abolfazl Ghasemzadeh</a>, <a href="/search/cs?searchtype=author&query=Magera%2C+F">Floriane Magera</a>, <a href="/search/cs?searchtype=author&query=Standaert%2C+B">Baptiste Standaert</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir Mohammad Mansourian</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xin Zhou</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</a>, <a href="/search/cs?searchtype=author&query=Ghanem%2C+B">Bernard Ghanem</a>, <a href="/search/cs?searchtype=author&query=Alahi%2C+A">Alexandre Alahi</a>, <a href="/search/cs?searchtype=author&query=Van+Droogenbroeck%2C+M">Marc Van Droogenbroeck</a>, <a href="/search/cs?searchtype=author&query=De+Vleeschouwer%2C+C">Christophe De Vleeschouwer</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="2404.11335v1-abstract-short" style="display: inline;"> Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11335v1-abstract-full').style.display = 'inline'; document.getElementById('2404.11335v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.11335v1-abstract-full" style="display: none;"> Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, reconstructing the game state from videos captured by a single camera is challenging. It requires understanding the position of the athletes and the viewpoint of the camera to localize and identify players within the field. In this work, we formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number. Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state reconstruction methods. Finally, we propose and release an end-to-end baseline for game state reconstruction, bootstrapping the research on this task. Our experiments show that GSR is a challenging novel task, which opens the field for future research. Our dataset and codebase are publicly available at https://github.com/SoccerNet/sn-gamestate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.11335v1-abstract-full').style.display = 'none'; document.getElementById('2404.11335v1-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2024 IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Work. (CVPRW) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.05451">arXiv:2403.05451</a> <span> [<a href="https://arxiv.org/pdf/2403.05451">pdf</a>, <a href="https://arxiv.org/format/2403.05451">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"> Attention-guided Feature Distillation for Semantic Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Jalali%2C+A">Arya Jalali</a>, <a href="/search/cs?searchtype=author&query=Ahmadi%2C+R">Rozhan Ahmadi</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2403.05451v2-abstract-short" style="display: inline;"> In contrast to existing complex methodologies commonly employed for distilling knowledge from a teacher to a student, this paper showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention. The proposed method has proven to be effective in distilling rich information, outperforming existing methods in semantic segmentation as a dense prediction t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05451v2-abstract-full').style.display = 'inline'; document.getElementById('2403.05451v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.05451v2-abstract-full" style="display: none;"> In contrast to existing complex methodologies commonly employed for distilling knowledge from a teacher to a student, this paper showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention. The proposed method has proven to be effective in distilling rich information, outperforming existing methods in semantic segmentation as a dense prediction task. The proposed Attention-guided Feature Distillation (AttnFD) method, employs the Convolutional Block Attention Module (CBAM), which refines feature maps by taking into account both channel-specific and spatial information content. Simply using the Mean Squared Error (MSE) loss function between the refined feature maps of the teacher and the student, AttnFD demonstrates outstanding performance in semantic segmentation, achieving state-of-the-art results in terms of improving the mean Intersection over Union (mIoU) of the student network on the PascalVoc 2012, Cityscapes, COCO, and CamVid datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05451v2-abstract-full').style.display = 'none'; document.getElementById('2403.05451v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">9 pages, 8 figures, and 6 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02474">arXiv:2402.02474</a> <span> [<a href="https://arxiv.org/pdf/2402.02474">pdf</a>, <a href="https://arxiv.org/format/2402.02474">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 Spectral Improvement for Unsupervised Image Instance Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arefi%2C+F">Farnoosh Arefi</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2402.02474v3-abstract-short" style="display: inline;"> Deep spectral methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention compared to other tasks within the context of deep spectral methods. This paper addresses the fact that not all channels… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02474v3-abstract-full').style.display = 'inline'; document.getElementById('2402.02474v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02474v3-abstract-full" style="display: none;"> Deep spectral methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention compared to other tasks within the context of deep spectral methods. This paper addresses the fact that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. In fact, Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules: Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A new similarity metric called Bray-Curtis over Chebyshev (BoC) is proposed to address this issue. It takes into account the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS2019 dataset highlight the improvements achieved by the proposed channel reduction methods and the use of BoC instead of the conventional dot product for creating the affinity matrix. These improvements are observed in terms of mean Intersection over Union and extracted instance segments, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02474v3-abstract-full').style.display = 'none'; document.getElementById('2402.02474v3-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">11 pages, 13 figures and 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.09942">arXiv:2401.09942</a> <span> [<a href="https://arxiv.org/pdf/2401.09942">pdf</a>, <a href="https://arxiv.org/format/2401.09942">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 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/10.1145/3606038.3616172">10.1145/3606038.3616172 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Somers%2C+V">Vladimir Somers</a>, <a href="/search/cs?searchtype=author&query=De+Vleeschouwer%2C+C">Christophe De Vleeschouwer</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2401.09942v1-abstract-short" style="display: inline;"> Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification syst… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09942v1-abstract-full').style.display = 'inline'; document.getElementById('2401.09942v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.09942v1-abstract-full" style="display: none;"> Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method outperforms all existing tracking methods on the challenging SoccerNet tracking dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09942v1-abstract-full').style.display = 'none'; document.getElementById('2401.09942v1-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> 18 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports (MMSports 2023), October 29, 2023, Ottawa, ON, Canada </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.00833">arXiv:2401.00833</a> <span> [<a href="https://arxiv.org/pdf/2401.00833">pdf</a>, <a href="https://arxiv.org/format/2401.00833">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"> Rethinking RAFT for Efficient Optical Flow </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Eslami%2C+N">Navid Eslami</a>, <a href="/search/cs?searchtype=author&query=Arefi%2C+F">Farnoosh Arefi</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2401.00833v1-abstract-short" style="display: inline;"> Despite significant progress in deep learning-based optical flow methods, accurately estimating large displacements and repetitive patterns remains a challenge. The limitations of local features and similarity search patterns used in these algorithms contribute to this issue. Additionally, some existing methods suffer from slow runtime and excessive graphic memory consumption. To address these pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00833v1-abstract-full').style.display = 'inline'; document.getElementById('2401.00833v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.00833v1-abstract-full" style="display: none;"> Despite significant progress in deep learning-based optical flow methods, accurately estimating large displacements and repetitive patterns remains a challenge. The limitations of local features and similarity search patterns used in these algorithms contribute to this issue. Additionally, some existing methods suffer from slow runtime and excessive graphic memory consumption. To address these problems, this paper proposes a novel approach based on the RAFT framework. The proposed Attention-based Feature Localization (AFL) approach incorporates the attention mechanism to handle global feature extraction and address repetitive patterns. It introduces an operator for matching pixels with corresponding counterparts in the second frame and assigning accurate flow values. Furthermore, an Amorphous Lookup Operator (ALO) is proposed to enhance convergence speed and improve RAFTs ability to handle large displacements by reducing data redundancy in its search operator and expanding the search space for similarity extraction. The proposed method, Efficient RAFT (Ef-RAFT),achieves significant improvements of 10% on the Sintel dataset and 5% on the KITTI dataset over RAFT. Remarkably, these enhancements are attained with a modest 33% reduction in speed and a mere 13% increase in memory usage. The code is available at: https://github.com/n3slami/Ef-RAFT <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00833v1-abstract-full').style.display = 'none'; document.getElementById('2401.00833v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">7 pages, 5 figures, 4 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> ACM-class: F.2.2; 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/2309.06006">arXiv:2309.06006</a> <span> [<a href="https://arxiv.org/pdf/2309.06006">pdf</a>, <a href="https://arxiv.org/ps/2309.06006">ps</a>, <a href="https://arxiv.org/format/2309.06006">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"> SoccerNet 2023 Challenges Results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cioppa%2C+A">Anthony Cioppa</a>, <a href="/search/cs?searchtype=author&query=Giancola%2C+S">Silvio Giancola</a>, <a href="/search/cs?searchtype=author&query=Somers%2C+V">Vladimir Somers</a>, <a href="/search/cs?searchtype=author&query=Magera%2C+F">Floriane Magera</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xin Zhou</a>, <a href="/search/cs?searchtype=author&query=Mkhallati%2C+H">Hassan Mkhallati</a>, <a href="/search/cs?searchtype=author&query=Deli%C3%A8ge%2C+A">Adrien Deli猫ge</a>, <a href="/search/cs?searchtype=author&query=Held%2C+J">Jan Held</a>, <a href="/search/cs?searchtype=author&query=Hinojosa%2C+C">Carlos Hinojosa</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Miralles%2C+P">Pierre Miralles</a>, <a href="/search/cs?searchtype=author&query=Barnich%2C+O">Olivier Barnich</a>, <a href="/search/cs?searchtype=author&query=De+Vleeschouwer%2C+C">Christophe De Vleeschouwer</a>, <a href="/search/cs?searchtype=author&query=Alahi%2C+A">Alexandre Alahi</a>, <a href="/search/cs?searchtype=author&query=Ghanem%2C+B">Bernard Ghanem</a>, <a href="/search/cs?searchtype=author&query=Van+Droogenbroeck%2C+M">Marc Van Droogenbroeck</a>, <a href="/search/cs?searchtype=author&query=Kamal%2C+A">Abdullah Kamal</a>, <a href="/search/cs?searchtype=author&query=Maglo%2C+A">Adrien Maglo</a>, <a href="/search/cs?searchtype=author&query=Clap%C3%A9s%2C+A">Albert Clap茅s</a>, <a href="/search/cs?searchtype=author&query=Abdelaziz%2C+A">Amr Abdelaziz</a>, <a href="/search/cs?searchtype=author&query=Xarles%2C+A">Artur Xarles</a>, <a href="/search/cs?searchtype=author&query=Orcesi%2C+A">Astrid Orcesi</a>, <a href="/search/cs?searchtype=author&query=Scott%2C+A">Atom Scott</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+B">Bin Liu</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+B">Byoungkwon Lim</a> , et al. (77 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="2309.06006v1-abstract-short" style="display: inline;"> The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06006v1-abstract-full').style.display = 'inline'; document.getElementById('2309.06006v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.06006v1-abstract-full" style="display: none;"> The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06006v1-abstract-full').style.display = 'none'; document.getElementById('2309.06006v1-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> 12 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.04243">arXiv:2308.04243</a> <span> [<a href="https://arxiv.org/pdf/2308.04243">pdf</a>, <a href="https://arxiv.org/format/2308.04243">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"> AICSD: Adaptive Inter-Class Similarity Distillation for Semantic Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Ahmadi%2C+R">Rozhan Ahmadi</a>, <a href="/search/cs?searchtype=author&query=Kasaei%2C+S">Shohreh Kasaei</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="2308.04243v1-abstract-short" style="display: inline;"> In recent years, deep neural networks have achieved remarkable accuracy in computer vision tasks. With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowledge distillation has emerged as a successful technique for improving the accuracy of lightweight student networks. The existing methods often neglect the information in channels and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.04243v1-abstract-full').style.display = 'inline'; document.getElementById('2308.04243v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.04243v1-abstract-full" style="display: none;"> In recent years, deep neural networks have achieved remarkable accuracy in computer vision tasks. With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowledge distillation has emerged as a successful technique for improving the accuracy of lightweight student networks. The existing methods often neglect the information in channels and among different classes. To overcome these limitations, this paper proposes a novel method called Inter-Class Similarity Distillation (ICSD) for the purpose of knowledge distillation. The proposed method transfers high-order relations from the teacher network to the student network by independently computing intra-class distributions for each class from network outputs. This is followed by calculating inter-class similarity matrices for distillation using KL divergence between distributions of each pair of classes. To further improve the effectiveness of the proposed method, an Adaptive Loss Weighting (ALW) training strategy is proposed. Unlike existing methods, the ALW strategy gradually reduces the influence of the teacher network towards the end of training process to account for errors in teacher's predictions. Extensive experiments conducted on two well-known datasets for semantic segmentation, Cityscapes and Pascal VOC 2012, validate the effectiveness of the proposed method in terms of mIoU and pixel accuracy. The proposed method outperforms most of existing knowledge distillation methods as demonstrated by both quantitative and qualitative evaluations. Code is available at: https://github.com/AmirMansurian/AICSD <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.04243v1-abstract-full').style.display = 'none'; document.getElementById('2308.04243v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">10 pages, 5 figures, 5 tables</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 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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