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Appl."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Out-of-distribution (OOD) detection is crucial for deploying models in open-world environments. This process aims to mitigate the issue of overconfident predictions, which is a common problem for models designed for closed-domain tasks when they encounter OOD data. Recent progress in OOD detection has shown that integrating auxiliary datasets during model training can greatly enhance OOD detection performance. However, existing methods tend to heavily depend on these auxiliary datasets to establish the decision boundary for in-distribution (ID) data, while not adequately addressing OOD object detection in safety\u2013critical applications. In this article, we propose object-level OOD detection and introduce a hierarchical hard negative sampling (HNS) strategy that does not necessitate auxiliary data. Specifically, we offer a new metric that strategically considers difficult negatives near the decision boundary between inter-class and intra-class instances. Inspired by adversarial thinking, we sample outliers for each class, ensuring that the negative samples capture both diversity and informative traits. We conducted comprehensive experiments on three public datasets. The results demonstrate that HNS performs superiorly in object-level OOD detection, even without auxiliary datasets. The source code can be accessed at:\n                    <jats:italic toggle=\"yes\">\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Aurevior1\/HNS\">https:\/\/github.com\/Aurevior1\/HNS<\/jats:ext-link>\n                      .\n                    <\/jats:italic>\n                  <\/jats:p>","DOI":"10.1145\/3785471","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T13:57:04Z","timestamp":1766584624000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hierarchical Hard Negative Sampling Strategy for Robust Out-of-Distribution Object Detection"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1094-621X","authenticated-orcid":false,"given":"Junteng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8188-8336","authenticated-orcid":false,"given":"Zizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China and  Beijing Institute of Astronautical Systems Engineering, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8381-8187","authenticated-orcid":false,"given":"Yunji","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4513-805X","authenticated-orcid":false,"given":"Sagar","family":"Samtani","sequence":"additional","affiliation":[{"name":"Kelley School of Business, Indiana University, Bloomington, Indiana, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8478-3932","authenticated-orcid":false,"given":"Yangyang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Social Computing, Academy of Cyber, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-610X","authenticated-orcid":false,"given":"Lei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information and Engineering, Chang\u2019an University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9905-3238","authenticated-orcid":false,"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1454","volume-title":"Proceedings of the 40th International Conference on Machine LearningProceedings of Machine Learning Research","volume":"202","author":"Bai Haoyue","year":"2023","unstructured":"Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. 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