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To address these issues, we introduce a novel weakly supervised cross-domain framework that enriches feature representations by explicitly integrating semantic cues from Human-Object Interactions (HOI) and action dynamics. Specifically, we model domain-specific features using two independent encoders and fuse them via a bi-directional cross-attention mechanism to obtain a unified embedding. To further alleviate class imbalance and enhance feature discrimination, our network is jointly optimized with classification loss, a cross-domain contrastive loss, and a clip-level focal loss. The proposed method is evaluated with various visual backbones, demonstrating its adaptability and robustness. Extensive experiments show that our approach achieves competitive performance against state-of-the-art methods under weak supervision in terms of AUC. Specifically, our proposed method achieves 98.33% AUC and 87.96% AUC on two benchmark datasets, i.e., ShanghaiTech and UCF-Crime, respectively.<\/jats:p>","DOI":"10.1371\/journal.pone.0358538","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:47:33Z","timestamp":1789753653000},"page":"e0358538","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learn the interactions: Weakly supervised video anomaly detection with human-object interactions"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7305-6282","authenticated-orcid":true,"given":"Maowen","family":"Zhou","sequence":"first","affiliation":[{"name":"Universiti Malaya","place":["Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia"],"department":["Department of Artificial Intelligence"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3026-9428","authenticated-orcid":true,"given":"Erma Rahayu Mohd","family":"Faizal Abdullah","sequence":"additional","affiliation":[{"name":"Universiti Malaya","place":["Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia"],"department":["Department of Artificial Intelligence"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-5400","authenticated-orcid":true,"given":"Aznul Qalid Md","family":"Sabri","sequence":"additional","affiliation":[{"name":"Universiti Malaya","place":["Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia"],"department":["Department of Artificial Intelligence"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanto","family":"Rahardja","sequence":"additional","affiliation":[{"name":"Engineering Cluster, Singapore Institute of Technology","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"pone.0358538.ref001","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v37i1.25112","article-title":"MGFN: magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection.","volume-title":"Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. 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