{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:23:58Z","timestamp":1792067038155,"version":"4.4.0"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2021289"],"award-info":[{"award-number":["2021289"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["AB22080054"],"award-info":[{"award-number":["AB22080054"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Key Research and Development Plan","award":["2021289"],"award-info":[{"award-number":["2021289"]}]},{"name":"Guangxi Key Research and Development Plan","award":["AB22080054"],"award-info":[{"award-number":["AB22080054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Weakly supervised video anomaly detection aims to identify abnormal events in video sequences without requiring frame-level supervision, which is a challenging task in computer vision. Traditional methods typically rely on low-level visual features with weak supervision from a single backbone branch, which often struggles to capture the distinctive characteristics of different categories. This limitation reduces their adaptability to real-world scenarios. In real-world situations, the boundary between normal and abnormal events is often unclear and context-dependent. For example, running on a track may be considered normal, but running on a busy road could be deemed abnormal. To address these challenges, RelVid is introduced as a novel framework that improves anomaly detection by expanding the relative feature gap between classes extracted from a single backbone branch. The key innovation of RelVid lies in the integration of auxiliary tasks, which guide the model to learn more discriminative features, significantly boosting the model\u2019s performance. These auxiliary tasks\u2014including text-based anomaly detection and feature reconstruction learning\u2014act as additional supervision, helping the model capture subtle differences and anomalies that are often difficult to detect in weakly supervised settings. In addition, RelVid incorporates two other components, which include class activation feature learning for improved feature discrimination and a temporal attention module for capturing sequential dependencies. This approach enhances the model\u2019s robustness and accuracy, enabling it to better handle complex and ambiguous scenarios. Evaluations on two widely used benchmark datasets, UCF-Crime and XD-Violence, demonstrate the effectiveness of RelVid. Compared to state-of-the-art methods, RelVid achieves superior performance in both detection accuracy and robustness.<\/jats:p>","DOI":"10.3390\/s25072037","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T08:42:35Z","timestamp":1742892155000},"page":"2037","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["RelVid: Relational Learning with Vision-Language Models for Weakly Video Anomaly Detection"],"prefix":"10.3390","volume":"25","author":[{"given":"Jingxin","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohan","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6934-3130","authenticated-orcid":false,"given":"Zhengyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4186-0678","authenticated-orcid":false,"given":"Xinrong","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy for Engineering and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. 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