{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:08:28Z","timestamp":1765357708190,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Domain adaptation has been extensively explored in object detection. Through the utilization of self-training and the decoupling of adversarial feature learning from the training of the detector, current methods make detectors more transferable and ensure their discriminability. However, the presence of low-quality pseudo labels during self-training introduces noises to the training phase and thus degrades the model performance. To tackle this challenge, we introduce an I-adapt framework, whose IoU Adapter accurately predicts the Intersection over Union (IoU) between predicted boxes and their corresponding ground-truth boxes in both source and target domains. This enables an effective measure for the pseudo-label quality. Based on this measure, we propose a re-weighting strategy, which enforces the detector to focus on learning from high-quality pseudo labels. We achieve state-of-the-art (SOTA) performance in several cross-domain object detection tasks, proving the effectiveness of I-adapt.<\/jats:p>","DOI":"10.3233\/faia240471","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:35:49Z","timestamp":1729168549000},"source":"Crossref","is-referenced-by-count":5,"title":["I-Adapt: Using IoU Adapter to Improve Pseudo Labels in Cross-Domain Object Detection"],"prefix":"10.3233","author":[{"given":"Qifeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Hunan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjian","family":"Chen","sequence":"additional","affiliation":[{"name":"Hunan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Hunan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuo","family":"Tang","sequence":"additional","affiliation":[{"name":"Hunan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240471","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:35:50Z","timestamp":1729168550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240471","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}