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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 4: AAAI-21 Technical Tracks 4

Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

February 1, 2023

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Authors

Kecheng Zheng

University of Science and Technology of China


Cuiling Lan

Microsoft Research


Wenjun Zeng

Microsoft Research


Zhizheng Zhang

University of Science and Technology of China


Zheng-Jun Zha

University of Science and Technology of China


DOI:

10.1609/aaai.v35i4.16468


Abstract:

Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation learning and deteriorate the performance. In this paper, we propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples. We build our baseline framework using the mean teacher method together with an additional contrastive loss. We have observed that a sample with a wrong pseudo-label through clustering in general has a weaker consistency between the output of the mean teacher model and the student model. Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the ID classification loss per sample, the triplet loss, and the contrastive loss. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.

Topics: AAAI

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HOW TO CITE:

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification Proceedings of the AAAI Conference on Artificial Intelligence (2021) 3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification AAAI 2021, 3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha (2021). Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. 2021. Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification. "Proceedings of the AAAI Conference on Artificial Intelligence". 3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. (2021) "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification", Proceedings of the AAAI Conference on Artificial Intelligence, p.3538-3546

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha, "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification", AAAI, p.3538-3546, 2021.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 3538-3546.

Kecheng Zheng||Cuiling Lan||Wenjun Zeng||Zhizheng Zhang||Zheng-Jun Zha. Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification. AAAI[Internet]. 2021[cited 2023]; 3538-3546.


ISSN: 2374-3468


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