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Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection

文献类型：期刊论文

作者Liu, Yabo1,2; Wang, Jinghua3; Xiao, Linhui2,4; Liu, Chengliang1; Wu, Zhihao1; Xu, Yong1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2023-11-28
页码12
ISSN号2162-237X
关键词Curriculum learning domain adaptive object detection location metric task disentanglement
DOI10.1109/TNNLS.2023.3331778
通讯作者Wang, Jinghua(wangjh2012@foxmail.com) ; Xu, Yong(yongxu@ymail.com)
英文摘要Unsupervised domain adaptive object detection (UDA-OD) is a challenging problem since it needs to locate and recognize objects while maintaining the generalization ability across domains. Most existing UDA-OD methods directly integrate the adaptive modules into the detectors. This integration procedure can significantly sacrifice the detection performances, though it enhances the generalization ability. To solve this problem, we propose an effective framework, named foregroundness-aware task disentanglement and self-paced curriculum adaptation (FA-TDCA), to disentangle the UDA-OD task into four independent subtasks of source detector pretraining, classification adaptation, location adaptation, and target detector training. The disentanglement can transfer the knowledge effectively while maintaining the detection performance of our model. In addition, we propose a new metric, i.e., foregroundness, and use it to evaluate the confidence of the location result. We use both foregroundness and classification confidence to assess the label quality of the proposals. For effective knowledge transfer across domains, we utilize a self-paced curriculum learning paradigm to train adaptors and gradually improve the quality of the pseudolabels associated with the target samples. Experiment results indicate that our method achieves state-of-the-art results on four cross-domain object detection tasks.
资助项目Natural Science Foundation China (NSFC)
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001120244700001
资助机构Natural Science Foundation China (NSFC)
源URL[http://ir.ia.ac.cn/handle/173211/55063]  
专题多模态人工智能系统全国重点实验室
通讯作者Wang, Jinghua; Xu, Yong
作者单位1.Harbin Inst Technol, Shenzhen Key Lab Visual Object Detect & Recognit, Shenzhen 518055, Peoples R China
2.Peng Cheng Lab, Shenzhen 518055, Peoples R China
3.Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Yabo,Wang, Jinghua,Xiao, Linhui,et al. Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:12.
APA Liu, Yabo,Wang, Jinghua,Xiao, Linhui,Liu, Chengliang,Wu, Zhihao,&Xu, Yong.(2023).Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA Liu, Yabo,et al."Foregroundness-Aware Task Disentanglement and Self-Paced Curriculum Learning for Domain Adaptive Object Detection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):12.

入库方式： OAI收割

来源：自动化研究所

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