Frequency-based pseudo-domain generation for domain generalizable object detection

doi:10.1016/j.neucom.2023.126265

	Frequency-based pseudo-domain generation for domain generalizable object detection
	Zhang, Siqi 1,2; Zhang, Lu1 ; Liu, Zhi-Yong1,2,3
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2023-07-14
卷号	542 页码:12
通讯作者	Liu, Zhi-Yong(zhiyong.liu@ia.ac.cn)
摘要	Domain generalizable object detection (DGOD) aims to train a detector that performs well on multiple unseen target domains, which is crucial for deploying the detector in practice. Recent methods for DGOD typically inherit the idea from domain adaptation to align or disentangle features, but these meth-ods struggle to handle unknown target distributions. In this paper, we propose a unified framework to tackle the DGOD task from a novel pseudo-domain generation perspective. Our framework comprises two stages: distribution diversification and domain-invariant feature learning. In the distribution diver-sification stage, we design a Frequency-based Pseudo-domain Generator (FPG) to construct the pseudo domain via excavating latent style information and enhancing semantic information in frequency space. The generated pseudo domain can provide diverse training distributions, which enhances generalization performance. In the domain-invariant feature learning stage, we introduce Rotation Prediction and Semantic Consistency (RPSC) learning, including an auxiliary self-supervised task rotation prediction to encourage generalized feature learning and a semantic consistency loss to enforce the detector to be invariant of domain shifts. Extensive experiments are conducted on various object detection benchmarks, demonstrating the superiority of our approach over state-of-the-art methods in both single-source and multi-source settings.(c) 2023 Elsevier B.V. All rights reserved.
关键词	Domain generalization Object detection Transfer learning Self-Supervised learning
DOI	10.1016/j.neucom.2023.126265
关键词[WOS]	ADAPTATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2020AAA0108902] ; Strategic Priority Research Program of Chinese Acad- emy of Sciences[XDB32050100] ; NSFC[62206288]
项目资助者	National Key Research and Development Plan of China ; Strategic Priority Research Program of Chinese Acad- emy of Sciences ; NSFC
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001003715300001
出版者	ELSEVIER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53421
专题	多模态人工智能系统全国重点实验室
通讯作者	Liu, Zhi-Yong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Nanjing Artificial Intelligence Res IA, Nanjing 211100, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhang, Siqi,Zhang, Lu,Liu, Zhi-Yong. Frequency-based pseudo-domain generation for domain generalizable object detection[J]. NEUROCOMPUTING,2023,542:12.
APA	Zhang, Siqi,Zhang, Lu,&Liu, Zhi-Yong.(2023).Frequency-based pseudo-domain generation for domain generalizable object detection.NEUROCOMPUTING,542,12.
MLA	Zhang, Siqi,et al."Frequency-based pseudo-domain generation for domain generalizable object detection".NEUROCOMPUTING 542(2023):12.