Name
Abstract | ||
|---|---|---|
Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks. |
Year | Venue | Keywords |
|---|---|---|
2022 | AAAI Conference on Artificial Intelligence | Computer Vision (CV),Machine Learning (ML) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhang Wu | 1 | 0 | 0.34 |
| Tengteng Huang | 2 | 0 | 0.34 |
| Haotian Yao | 3 | 0 | 0.34 |
| Chi Zhang | 4 | 20 | 2.47 |
| Yuanjie Shao | 5 | 24 | 5.72 |
| Chuchu Han | 6 | 13 | 4.73 |
| Changxin Gao | 7 | 188 | 38.01 |
| Nong Sang | 8 | 475 | 72.22 |