{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:25:35Z","timestamp":1760059535301,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076246","2024JKF11"],"award-info":[{"award-number":["62076246","2024JKF11"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62076246","2024JKF11"],"award-info":[{"award-number":["62076246","2024JKF11"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global\u2013local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset.<\/jats:p>","DOI":"10.3390\/e27070655","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T05:17:42Z","timestamp":1750396662000},"page":"655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination"],"prefix":"10.3390","volume":"27","author":[{"given":"Chunxiang","family":"Niu","sequence":"first","affiliation":[{"name":"College of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","first-page":"18","article-title":"Anomaly detection and localization in crowded scenes","volume":"36","author":"Li","year":"2013","journal-title":"IEEE Trans. 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