This paper aims to learn a domain-generalizable (DG) person re-identification (ReID) representation from large-scale videos without any annotation. Prior DG ReID methods employ limited labeled data for training due to the high cost of annotation, which restricts further advances. To overcome the barriers of data and annotation, we propose to utilize large-scale unsupervised data for training. The key issue lies in how to mine identity information. To this end, we propose an Identity-seeking Self-supervised Representation learning (ISR) method. ISR constructs positive pairs from inter-frame images by modeling the instance association as a maximum-weight bipartite matching problem. A reliability-guided contrastive loss is further presented to suppress the adverse impact of noisy positive pairs, ensuring that reliable positive pairs dominate the learning process. The training cost of ISR scales approximately linearly with the data size, making it feasible to utilize large-scale data for training. The learned representation exhibits superior generalization ability. Without human annotation and fine-tuning, ISR achieves 87.0% Rank-1 on Market-1501 and 56.4% Rank-1 on MSMT17, outperforming the best supervised domain-generalizable method by 5.0% and 19.5%, respectively. In the pre-training→fine-tuning scenario, ISR achieves state-of-the-art performance, with 88.4% Rank-1 on MSMT17. The code is at https://github.com/dcp15/ISR_ICCV2023_Oral.

Person re-identification (re-ID) aims to match pedestrian images from non-overlapping cameras. It is a challenging task because of the feature misalignment problem caused by occlusion. In this paper, inspired by the coarse-to-fine nature of human perception, we propose a novel Progressive-Granularity Retrieval (PGR) method to tackle this issue. Specifically, (i) we define instance-level, part-level and pixel-level features for an image. PGR learns these features by a single feature extractor to capture hierarchical clues in the image. (ii) These features are inherently related but different in perceptual granularity, and they can provide complementary information. For each type of feature, we propose a corresponding similarity metric to achieve hierarchical feature alignment. (iii) In training, we learn the model end-to-end. In inference, a progressive retrieval strategy is introduced to efficiently aggregate the complementary information provided by these features. Extensive experiments on three bench-marks of both occluded and holistic-body re-ID tasks show the effectiveness of the proposed method. Especially, our method significantly outperforms state-of-the-art by 4.5% Rank-1 score on the challenging Occluded-Duke dataset.

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself, which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as ``feature cloud''. The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.