{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:51Z","timestamp":1761176211504,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Although DETR-based methods have achieved considerable success in semi-supervised object detection (SSOD), several challenges remain unresolved: (1) To obtain higher-quality pseudo-labels for training, teacher models often adopt larger-scale architectures, which severely impacts inference speed. In contrast, lightweight detectors may compromise the effectiveness of existing SSOD methods. (2) Bipartite matching utilizing the Hungarian algorithm does not fully leverage potentially valuable pseudo-labels. (3) Current methods alleviate the negative impact of low-quality pseudo-labels through one-to-many assignment, yet this often leads to issues such as duplicate detections. To tackle these challenges, we propose a lightweight end-to-end semi-supervised object detection framework called Efficient Semi-DETR. Specifically, to improve accuracy while reducing inference latency, we introduce a heterogeneous teacher-student framework, which leverages a Collaborative Auxiliary Head to better mine potentially valuable pseudo-labels. To tackle the issue of duplicate detections, we propose Efficient Query Matching to improve training efficiency and enhance the detection of small objects. Notably, Efficient Semi-DETR achieves 44.92 mAP with only 10% of the annotated MS-COCO data, surpassing state-of-the-art methods, while its inference latency is only a quarter of existing methods.<\/jats:p>","DOI":"10.3233\/faia251111","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:47Z","timestamp":1761126707000},"source":"Crossref","is-referenced-by-count":0,"title":["Efficient Semi-DETR: Real Time End-to-End Semi-Supervised Object Detection"],"prefix":"10.3233","author":[{"given":"Zihao","family":"Xin","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]},{"given":"Wentong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]},{"given":"Jie","family":"Qin","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]},{"given":"Shengjun","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:48Z","timestamp":1761126708000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251111","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}