The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Adaptive Transfer Network for Cross-Domain Person Re-Identification
Abstract
Recent deep learning based person re-identification approaches have steadily improved the performance for benchmarks, however they often fail to generalize well from one domain to another. In this work, we propose a novel adaptive transfer network (ATNet) for effective cross-domain person re-identification. ATNet looks into the essential causes of domain gap and addresses it following the principle of "divide-and-conquer". It decomposes the complicated cross-domain transfer into a set of factor-wise sub-transfers, each of which concentrates on style transfer with respect to a certain imaging factor, e.g., illumination, resolution and camera view etc. An adaptive ensemble strategy is proposed to fuse factor-wise transfers by perceiving the affect magnitudes of various factors on images. Such "decomposition-and-ensemble" strategy gives ATNet the capability of precise style transfer at factor level and eventually effective transfer across domains. In particular, ATNet consists of a transfer network composed by multiple factor-wise CycleGANs and an ensemble CycleGAN as well as a selection network that infers the affects of different factors on transferring each image. Extensive experimental results on three widely-used datasets, i.e., Market-1501, DukeMTMC-reID and PRID2011 have demonstrated the effectiveness of the proposed ATNet with significant performance improvements over state-of-the-art methods.
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Cites
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Image-to-Image Translation with Conditional Adversarial Networks
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Cited by
Style Normalization and Restitution for Generalizable Person Re-Identification
Jin, X., Lan, C., Zeng, W., Chen, Z., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00321 · Cited by 426
Learning to Generate Novel Domains for Domain Generalization
Zhou, K., Yang, Y., Hospedales, T., & Xiang, T. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58517-4_33 · Cited by 382
Neural Feature Search for RGB-Infrared Person Re-Identification
Chen, Y., Wan, L., Li, Z., Jing, Q., & Sun, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00065 · Cited by 253
Metadata
- Authors
- Liu, J., Zha, Z.-J., Chen, D., Hong, R., & Wang, M.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 7195-7204
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2957037461
- Cited by
- 305
- References
- 46
- FWCI
- 17.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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