Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection
Abstract
Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.
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To obtain domain-invariant feature representation, feature regularization and adversarial training based methods have been explored at the image, category, or object levels.
“To obtain domain-invariant feature representation, feature regularization and adversarial training based methods have been explored at the image, category, or object levels [216-221].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [218]
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Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
“To obtain domain-invariant feature representation, feature regularization and adversarial training-based methods have been explored at the image, category, or object levels [216], [217], [218], [219], [220], [221].”
- Authors
- Hou, L., Zhang, Y., Fu, K., & Li, J.
- Affiliations
- State Key Laboratory of Virtual Reality Technology and Systems (CN)
- Sensetime (China) (CN)
- Peng Cheng Laboratory (CN)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 9924-9933
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3176920308
- Cited by
- 19
- References
- 50
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 5 · 2023: 9 · 2022: 1 · 2021: 1
- FWCI
- 1.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- co-attention, regional competition, knowledge transfer, image-level labels, domain adaptation
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