The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
Open accessAbstract
Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt, we present a "learning via translation" framework. In the baseline, we translate the labeled images from source to target domain in an unsupervised manner. We then train re-ID models with the translated images by supervised methods. Yet, being an essential part of this framework, unsupervised image-image translation suffers from the information loss of source-domain labels during translation. Our motivation is two-fold. First, for each image, the discriminative cues contained in its ID label should be maintained after translation. Second, given the fact that two domains have entirely different persons, a translated image should be dissimilar to any of the target IDs. To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image. Both constraints are implemented in the similarity preserving generative adversarial network (SPGAN) which consists of an Siamese network and a CycleGAN. Through domain adaptation experiment, we show that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
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Deep Residual Learning for Image Recognition
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
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Domain-Adversarial Training of Neural Networks
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Self-citation
Cited by
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Self-citationA survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification
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Self-citationGeneralizing a Person Retrieval Model Hetero- and Homogeneously
Zhong, Z., Zheng, L., Li, S., & Yang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_11 · Cited by 498
Self-citationApplications of Generative Adversarial Networks (GANs): An Updated Review
Alqahtani, H., Kavakli-Thorne, M., & Kumar, G. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-019-09388-y · Cited by 431
Metadata
- Authors
- Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., & Jiao, J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 994-1003
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963000559
- Cited by
- 1,011
- References
- 54
- FWCI
- 40.54
- Type
- conference paper
- Language
- en
- Open access
- green
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- Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., & Jiao, J. (2018). Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00110
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