Despite recent advancements in deep neural networks for point cloud recognition, real-world safety-critical applications present challenges due to unavoidable data corruption. Current models often fall short in generalizing to unforeseen distribution shifts. In this study, we harness the inherent set property of point cloud data to introduce a novel critical subset identification (CSI) method, aiming to bolster recognition robustness in the face of data corruption. Our CSI framework integrates two pivotal components: density-aware sampling (DAS) and self-entropy minimization (SEM), which cater to static and dynamic CSI, respectively. DAS ensures efficient robust anchor point sampling by factoring in local density, while SEM is employed during training to accentuate the most salient point-to-point attention. Evaluations reveal that our CSI approach yields error rates of 18.4\% and 16.3\% on ModelNet40-C and PointCloud-C, respectively, marking a notable improvement over state-of-the-art methods by margins of 5.2\% and 4.2\% on the respective benchmarks. Code is available at \href{https://github.com/masterwu2115/CSI/tree/main}{https://github.com/masterwu2115/CSI/tree/main}
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；College of Computing, City University of Hong Kong（香港城市大学计算机学院）；SWJTU-Leeds Joint School, Southwest Jiaotong University（西南交通大学-利兹学院）
Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection
School of Electrical & Computer Engineering, National Technical University Athens（雅典国立技术大学电气与计算机工程学院）；Hellenic Drones S.A.（希腊无人机公司）；School of Electronic Engineering & Computer Science, Queen Mary University of London（伦敦大学玛丽皇后学院电子工程与计算机科学学院）
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
College of Engineering, Peking University（北京大学工学院）；Third Hospital, Peking University Health Science Center（北京大学医学部第三医院）；Institute of Biomedical Engineering, Beijing Institute of Collaborative Innovation（北京协同创新研究院生物医学工程研究所）
TinySense+: Entropy-Aware CSI Compression for Scalable and Adaptive Wi-Fi Sensing
Smart Green Transformation Center (GREEN-X)（智能绿色转型中心（GREEN-X））；Department of Electrical & Computer Engineering（电气与计算机工程系）；School of Computer Science and Statistics（计算机科学与统计学学院）
Enhancing Plasticity for First Session Adaptation Continual Learning
LTCI, Télécom-Paris, Institut Polytechnique de Paris（Télécom-Paris 工程院）；University of Bergamo（贝加莫大学）；Inria at University Grenoble Alpes, LJK（格勒诺布尔阿尔卑斯大学）
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
Johns Hopkins University（约翰霍普金斯大学）；The Chinese University of Hong Kong（香港中文大学）；Intuitive Surgical Inc.（Intuitive Surgical公司）；University of Arizona（亚利桑那大学）；Chonnam National University（全州大学）；University of British Columbia（不列颠哥伦比亚大学）；Kyung Hee University（庆熙大学）；University H（大学H）
Generalizable Image Repair for Robust Visual Control
University of Florida AI Scholars Program（佛罗里达大学人工智能学者计划）；Trustworthy Engineered Autonomy (TEA) Lab（可信工程自主性实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
