CASIA OpenIR  > 多模态人工智能系统全国重点实验室
DomainFeat: Learning Local Features With Domain Adaptation
Xu, Rongtao1,2; Wang, Changwei1,2; Xu, Shibiao2; Meng, Weiliang1,2; Zhang, Yuyang3; Fan, Bin4; Zhang, Xiaopeng1,2
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2024
Volume34Issue:1Pages:46-59
Corresponding AuthorXu, Shibiao(shibiaoxu@bupt.edu.cn)
AbstractAccurate and efficient keypoint detection and description is a fundamental step in various computer vision tasks. In this paper, we extract robust descriptors and detect accurate keypoints by learning local Features with Domain adaptation (DomainFeat). Specifically, our Domainfeat includes image-level domain invariance supervision, pixel-level domain consistency supervision, Pixel-Adaptive keypoint Detection(PA-Det), and cross-domain dataset with domain stable point supervision. First, we introduce the image-level domain invariance supervision to make the high-level feature distributions from different domains close by fusing domain-invariant representations in the decoder. Furthermore, to compensate for the inconsistency between descriptors corresponding to the keypoints at the pixel level, we propose the pixel-level domain consistency supervision. Then we present the Pixel-Adaptive keypoint Detection to efficiently detect accurate keypoints, which can improve accuracy by enhancing the local consistency of heatmaps. Finally, we propose an efficient approach to construct data and supervision labels in diverse domains, which can tackle complex application scenarios. With these novel modules and supervision methods, our DomainFeat can make feature detectors more accurate and descriptors more robust. Extensive experiments confirm that Domainfeat achieves state-of-the-art performance on benchmarks such as Aachen-Day-Night localization, HPatches image matching, and the challenging DNIM dataset.
KeywordFeature extraction Location awareness Visualization Robustness Image matching Detectors Decoding Local features domain adaptation cross-domain data consistency loss
DOI10.1109/TCSVT.2023.3282956
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:001138814400010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55537
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorXu, Shibiao
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.Thunder Software Technol Co Ltd, Chengdu 610000, Peoples R China
4.Univ Sci & Technol Beijing, Sch Intelligence Sci & Technol, Beijing 100083, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Rongtao,Wang, Changwei,Xu, Shibiao,et al. DomainFeat: Learning Local Features With Domain Adaptation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2024,34(1):46-59.
APA Xu, Rongtao.,Wang, Changwei.,Xu, Shibiao.,Meng, Weiliang.,Zhang, Yuyang.,...&Zhang, Xiaopeng.(2024).DomainFeat: Learning Local Features With Domain Adaptation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,34(1),46-59.
MLA Xu, Rongtao,et al."DomainFeat: Learning Local Features With Domain Adaptation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34.1(2024):46-59.
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