Multi-Level Attention for Unsupervised Person Re-Identification
This work addresses a specific issue in unsupervised person re-identification for computer vision applications, presenting an incremental improvement over existing attention mechanisms.
The paper tackles the problem of attention spreading in unsupervised person re-identification by proposing a multi-level attention block that combines head-level, pixel-level, and domain-level attention modules, and validates its performance on large datasets including Market-1501, DukeMTMC-reID, MSMT17, and PersonX.
The attention mechanism is widely used in deep learning because of its excellent performance in neural networks without introducing additional information. However, in unsupervised person re-identification, the attention module represented by multi-headed self-attention suffers from attention spreading in the condition of non-ground truth. To solve this problem, we design pixel-level attention module to provide constraints for multi-headed self-attention. Meanwhile, for the trait that the identification targets of person re-identification data are all pedestrians in the samples, we design domain-level attention module to provide more comprehensive pedestrian features. We combine head-level, pixel-level and domain-level attention to propose multi-level attention block and validate its performance on for large person re-identification datasets (Market-1501, DukeMTMC-reID and MSMT17 and PersonX).