{"type":"ScholarlyArticle","title":"OD-DETR: Online Distillation for Stabilizing Training of Detection Transformer","authors":[{"name":"Shengjian Wu","slug":"shengjian-wu","given":"Shengjian","family":"Wu","affiliation":"Shanghai Key Laboratory of Multidimensional Information Processing , East China Normal University; Finvolution Group"},{"name":"Li Sun","slug":"li-sun","given":"Li","family":"Sun","affiliation":"Shanghai Key Laboratory of Multidimensional Information Processing , East China Normal University; Key Laboratory of Advanced Theory and Application in Statistics and Data Science , East China Normal University; Main Group Auxiliary Group Choose Aux"},{"name":"Qingli Li","slug":"qingli-li","given":"Qingli","family":"Li","affiliation":"Shanghai Key Laboratory of Multidimensional Information Processing , East China Normal University"}],"abstract":"DEtection TRansformer (DETR) becomes a dominant paradigm, mainly due to its common architecture with high accuracy and no post-processing. However, DETR suffers from unstable training dynamics. It consumes more data and epochs to converge compared with CNN-based detectors. This paper aims to stabilize DETR training through the online distillation. It utilizes a teacher model, accumulated by Exponential Moving Average (EMA), and distills its knowledge into the online model in following three aspects. First, the matching relation between object queries and ground truth (GT) boxes in the teacher is employed to guide the student, so queries within the student are not only assigned labels based on their own predictions, but also refer to the matching results from the teacher. Second, the teacher's initial query is given to the online student, and its prediction is directly constrained by the corresponding output from the teacher. Finally, the object queries from teacher's different decoding stages are used to build the auxiliary groups to accelerate the convergence. For each GT, two queries with the least matching costs are selected into this extra group, and they predict the GT box and participate the optimization. Extensive experiments show that the proposed OD-DETR successfully stabilizes the training, and significantly increases the performance without bringing in more parameters.","doi":"10.24963/ijcai.2024/160","url":"https://ijcai.org/proceedings/2024/160","pdf":"https://ijcai.org/proceedings/2024/0160.pdf","markdown":"https://ijcai.org/proceedings/2024/160.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/160","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":160,"pages":{"first":1443,"last":1451},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Berthelot et al., 2019] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 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