{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:54:34Z","timestamp":1775325274433,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iros60139.2025.11245852","type":"proceedings-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:54:45Z","timestamp":1764269685000},"page":"9870-9877","source":"Crossref","is-referenced-by-count":1,"title":["Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection"],"prefix":"10.1109","author":[{"given":"Weicheng","family":"He","sequence":"first","affiliation":[{"name":"Université Grenoble Alpes,Grenoble,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changshun","family":"Wu","sequence":"additional","affiliation":[{"name":"Université Grenoble Alpes,Grenoble,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Hong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Chalmers University of Technology,Gothenburg,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Liverpool,Liverpool,UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saddek","family":"Bensalem","sequence":"additional","affiliation":[{"name":"CSX-AI,Grenoble,France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Hendrycks"},{"key":"ref2","article-title":"Towards unknown-aware learning with virtual outlier synthesis","volume-title":"International Conference on Learning Representations (ICLR)","author":"Du"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02154"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801584"},{"key":"ref5","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Advances in neural information processing systems (NeurIPS)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-74234-7_15"},{"key":"ref7","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Liang"},{"key":"ref8","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in neural information processing systems (NeurIPS)"},{"key":"ref9","article-title":"Scaling for training time and post-hoc out-of-distribution detection enhancement","volume-title":"International Conference on Learning Representations (ICLR)","author":"Xu"},{"key":"ref10","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume":"31","author":"Lee","year":"2018","journal-title":"Advances in neural information processing systems (NeurIPS)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8714971"},{"key":"ref12","first-page":"20827","article-title":"Out-of-distribution detection with deep nearest neighbors","volume-title":"International Conference on Machine Learning (ICML)","author":"Sun"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44267-4_2"},{"key":"ref14","article-title":"Likelihood ratios for out-of-distribution detection","volume":"32","author":"Ren","year":"2019","journal-title":"Advances in neural information processing systems (NeurIPS)"},{"key":"ref15","first-page":"22908","article-title":"A geometric explanation of the likelihood ood detection paradox","volume-title":"International Conference on Machine Learning (ICML)","author":"Kamkari"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02117-4"},{"key":"ref17","first-page":"32598","article-title":"Openood: Benchmarking generalized out-of-distribution detection","volume":"35","author":"Yang","year":"2022","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref18","first-page":"8759","article-title":"Scaling out-of-distribution detection for real-world settings","volume-title":"International Conference on Machine Learning (ICML)","author":"Hendrycks"},{"key":"ref19","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Hendrycks"},{"key":"ref20","article-title":"Adversarial training and robustness for multiple perturbations","volume":"32","author":"Tramer","year":"2019","journal-title":"Advances in neural information processing systems (NeurIPS)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref25","article-title":"Caltech 256","author":"Griffin","year":"2022"},{"key":"ref26","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2025","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"}],"event":{"name":"2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","location":"Hangzhou, China","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11245651\/11245652\/11245852.pdf?arnumber=11245852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T06:08:25Z","timestamp":1765519705000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11245852\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/iros60139.2025.11245852","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}