Name
Title | ||
|---|---|---|
Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification |
Abstract | ||
|---|---|---|
Person Re-identification (ReID) is an important yet challenging task in computer vision. Due to diverse background clutters, variations of viewpoints and body poses, it is far from being solved. How to extract discriminative and robust features invariant to background clutters is one of the core problems. In this paper, we first introduce a set of binary segmentation masks to construct synthetic RGB-Mask pairs as inputs, and then design a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions. Moreover, we propose a novel region-level triplet loss to guide the features learning, i.e., pulling the features from the full image and body region close, whereas pushing the features from backgrounds away. To learn the similarities from multiple features of the proposed MGCAM, we further introduce the instance-level two-stream metric co-learning (TSMCL) to help learn pair-wise relations between features from not only different regions but also different instances. TSMCL could help learn more compact features across full and body streams, enhancing the performance of MGCAM. We evaluate the proposed method on four public datasets, including MARS, Market-1501, CUHK03, and DukeMTMC-reID. Extensive experiments show that the proposed method is effective and achieves satisfying results. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.09.038 | Neurocomputing |
Keywords | DocType | Volume |
Person ReID,Contrastive attention model,Two-stream metric learning | Journal | 465 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunfeng Song | 1 | 54 | 8.53 |
| Caifeng Shan | 2 | 1681 | 80.01 |
| Yan Huang | 3 | 226 | 27.65 |
| Liang Wang | 4 | 0 | 0.34 |