Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023, 2023-July, pp. 2423-2428
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Camera_Proxy_based_Contrastive_Learning_with_Hard_Sampling_for_Unsupervised_Person_Re-identification.pdf | Published version | 2.28 MB |
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Because of the advantages of dealing with large scale unlabelled data unsupervised learning has recently attracted more attention for person re identification Particularly the combination of the unsupervised learning paradigm with contrastive learning shows promising efficiency in network optimization This work adopts the successful camera aware contrastive learning approach and further explores its capability on the camera proxy level to improve the data pair consistency Thus it is more robust to the camera change which still challenges the unsupervised person re identification This work proposed a Camera Proxy based Contrastive Learning framework which explicitly considers inter camera scenario and intra camera scenario Moreover this work is motivated by the strategy of selecting a hard negative sample in triplet loss learning and further extends it to contrastive learning for both negative and positive pair creation on the camera proxy level Extensive experiments demonstrate the superiority of the proposed framework over state of the art approaches on purely unsupervised re identification
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