Partially Relevant Video Retrieval (PRVR) seeks videos where only part of the content matches a text query. Existing methods treat every annotated text-video pair as a positive and all others as negatives, ignoring the rich semantic variation both within a single video and across different videos. Consequently, embeddings of both queries and their corresponding video-clip segments for distinct events within the same video collapse together, while embeddings of semantically similar queries and segments from different videos are driven apart. This limits retrieval performance when videos contain multiple, diverse events. This paper addresses the aforementioned problems, termed as semantic collapse, in both the text and video embedding spaces. We first introduce Text Correlation Preservation Learning, which preserves the semantic relationships encoded by the foundation model across text queries. To address collapse in video embeddings, we propose Cross-Branch Video Alignment (CBVA), a contrastive alignment method that disentangles hierarchical video representations across temporal scales. Subsequently, we introduce order-preserving token merging and adaptive CBVA to enhance alignment by producing video segments that are internally coherent yet mutually distinctive. Extensive experiments on PRVR benchmarks demonstrate that our framework effectively prevents semantic collapse and substantially improves retrieval accuracy.
Pairing Regularization for Mitigating Many-to-One Collapse in GANs
Institute of Communications Engineering, National Yang Ming Chiao Tung University；Department of Electrical \& Computer Engineering, Texas A\&M University
Beyond Semantic Priors: Mitigating Optimization Collapse for Generalizable Visual Forensics
Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学信息与科学学院）
University of Tehran（德黑兰大学）；LMU Munich（慕尼黑大学）；Google Brain（谷歌大脑）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）
Differentiable neural architecture search (DARTS) is a popular method for neural architecture search (NAS), which performs cell-search and utilizes continuous relaxation to improve the search efficiency via gradient-based optimization. The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining quality during search. Performance collapse has become an important topic of research, with many methods trying to solve the issue through either regularization or fundamental changes to DARTS. However, the weight-sharing framework used fo…
Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging
School of Computation, Information and Technology, Technical University of Munich, Germany（慕尼黑技术大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany（生物医学成像中的机器学习研究所，海德堡慕尼黑德国）；School of Biomedical Engineering and Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI——Konrad Zuse可靠性人工智能卓越学院）；TUM University Hospital Rechts der Isar（慕尼黑技术大学医院Rechts der Isar）
Coastal Carolina University（海岸卡罗莱纳大学）；University of South Alabama（南阿拉巴马大学）
On June 24, 2021, a 12-story condominium building (Champlain Towers South) in Surfside, Florida partially collapsed, resulting in one of the deadliest building collapses in United States history with 98 people confirmed deceased. In this work, we analyze the collapse event using a video clip that is publicly available from social media. In our analysis, we apply computer vision algorithms to corroborate new information from the video clip that may not be readily interpreted by human eyes. By comparing the differential features against different video frames, our proposed method is used to quan…
