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Confidence-adapted meta-interaction for unsupervised person re-identification

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Abstract

Most unsupervised person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature learning, and perform the two steps in an alternating fashion for training ReID models. However, incorrect/noisy pseudo-labels are often present due to various variations (e.g., human pose, illumination, and viewpoint, etc.). Such noisy pseudo-labels may harm the trained ReID models. In order to use diverse variations/information while minimizing negative influence of the noisy pseudo-labels, we propose a confidence-adapted meta-interaction (CAMI) method by explicitly exploring the interaction between the believable supervision (reliable pseudo-labels) and the diverse information. Specifically, CAMI iteratively trains the ReID model in a meta-learning manner, in which the training images are dynamically divided into a reliable set and an unreliable set. At each iteration, the pseudo-labels of images are predicted by clustering and the training images are divided by the proposed confidence-adapted sample disentanglement (CASD) method. To adapt the changes of the pseudo-labels and gradually refine the division, the CASD method dynamically predicts the pseudo-label confidence. It divides the training images into the reliable set (with high confidence pseudo-labels) and the unreliable set (with low confidence pseudo-labels), respectively. Then a meta-interaction method is proposed for training the ReID model, which consists of a meta-training step to use the believable supervision of the reliable set and a meta-testing step to use the diverse information of the unreliable set. Meanwhile, a bridge model is dynamically built to refine the unreliable set based on the believable supervision from the reliable set. The CAMI is evaluated by two unsupervised person ReID settings, including the image-based and the video-based. The experimental results on four datasets demonstrate the superiority of the proposed CAMI.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant U2034211 and Grant 62006017.

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Authors and Affiliations

  1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, No.3 Shangyuancun Haidian District, Beijing, 100044, Beijing, China

    Xiaobao Li, Qingyong Li, Wenyuan Xue, Yang Liu, Fengjiao Liang & Wen Wang

  2. School of Computer Science and Technology, Jiangsu Normal University, No.101 Shanghai Road Tongshan District, Xuzhou, 221116, Jiangsu, China

    Xiaobao Li

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  1. Xiaobao Li
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  2. Qingyong Li
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  4. Yang Liu
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Correspondence to Wen Wang.

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Appendices

Appendices

In this section, we give further analysis of the CAMI. There are three aspects. (1) Evaluation of the weighted contrastive loss and the mean teacher model. (2) Evaluation on other clustering approaches. (3) t-SNE visualization for person image features.

1.1 Further ablation study

Effectiveness of the weighted contrastive loss. To evaluate the impact of the weight in the weighted contrastive loss, we remove the weight from the weighted contrastive loss when training, i.e., ‘CAMI w/o weight’. As shown in Table 4, CAMI shows better performance than ‘CAMI w/o weight’. Specifically, it brings 3.0% and 2.9% improvements in mAP, and 2.3% and 0.8% in Rank-1 accuracy on both datasets, respectively. The improvement is because the weighted contrastive loss considers the reliability of the pseudo-labels and thus can further relieve the negative influence of the noisy pseudo-labels.

Table 4 Evaluation of the weighted contrastive loss and mean teacher model
Full size table

Effectiveness of the mean teacher. As a basic component in our proposed framework, the mean teacher model is also experimentally evaluated. As shown in Table 4, we observe consistent performance decline on both datasets when removing the mean teacher model. The mean teacher model and the weighted contrastive loss help us to build a strong unsupervised person ReID framework.

Table 5 Evaluation of the baseline and the CAMI on different clustering methods
Full size table
Fig. 10
figure 10

Evaluation of the pseudo-labels predicted by different clustering methods. NMI refers to normalized mutual information

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Fig. 11
figure 11

t-SNE visualizations of the baseline and CAMI on Market-1501. The colors of nodes denote person identities

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1.2 Evaluation on other clustering methods

To verify the effectiveness of the proposed CAMI on different clustering algorithms, we conduct experiments by replacing the DBSCAN algorithm with two other clustering approaches, including agglomerative clustering (AC) [48] and informap [49]. The results are reported in Table 5. Compared to the baseline, we observe the consistent superiority of the CAMI on all clustering methods. DBSCAN achieves better performance among these clustering approaches, and we adopt it as our pseudo-label generator as existing works [8].

We also evaluate the pseudo-labels predicted by different clustering methods using normalized mutual information (NMI). The NMI scores are shown in Fig. 10. As the training goes on, we can see that the NMI scores steadily rise. It means that the accuracy of the predicted pseudo-labels gradually increases. Furthermore, compared to the baseline, the CAMI consistently achieves higher NMI scores on different clustering approaches.

1.3 t-SNE visualization for person image features

We also visualize person image features of the baseline and CAMI by using t-SNE visualization. We sample 10 different identities from market-1501, each containing a variable number of images. The visualized results are exhibited in Fig. 11. It can be noticed that CAMI shows better intra-class compactness and inter-class separation than the baseline does. It suggests that CAMI can train more robust ReID model.

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Li, X., Li, Q., Xue, W. et al. Confidence-adapted meta-interaction for unsupervised person re-identification. Appl Intell 53, 25525–25542 (2023). https://doi.org/10.1007/s10489-023-04863-3

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