Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
Open accessAbstract
We introduce a novel unsupervised domain adaptation approach for object detection. We aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source-biased discriminativity problem of feature-level adaptations simultaneously. Our approach is composed of two stages, i.e., Domain Diversification (DD) and Multi-domain-invariant Representation Learning (MRL). At the DD stage, we diversify the distribution of the labeled data by generating various distinctive shifted domains from the source domain. At the MRL stage, we apply adversarial learning with a multi-domain discriminator to encourage feature to be indistinguishable among the domains. DD addresses the source-biased discriminativity, while MRL mitigates the imperfect image translation. We construct a structured domain adaptation framework for our learning paradigm and introduce a practical way of DD for implementation. Our method outperforms the state-of-the-art methods by a large margin of 3%~11% in terms of mean average precision (mAP) on various datasets.
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Cycle-consistent transformation has also been applied to bridge the gap between source and target domain.
“Cycle-consistent transformation [222] has also been applied to bridge the gap between source and target domain [223, 224].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [223]
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“Cycle-consistent transformation [222] has also been applied to bridge the gap between source and target domains [223], [224].”
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- Authors
- Kim, T., Jeong, M., Kim, S., Choi, S., & Kim, C.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 12448-12457
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2969583814
- Cited by
- 352
- References
- 50
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 51 · 2023: 69 · 2022: 77 · 2021: 73 · 2020: 38 · 2019: 6 · 2017: 1
- FWCI
- 18.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion (2017-0-01772)
- 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
- unsupervised domain adaptation, object detection, adversarial learning, cross-domain image translation
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