Learning Informative Attention Weights for Person Re-Identification

Attention mechanisms are widely used in deep learning and have been incorporated into DNNs for person Re-Identification (Re-ID) to enhance discriminative feature learning. Existing attention modules, including self-attention and channel attention, learn attention weights that quantify the importance of feature tokens or feature channels. However, existing attention methods do not explicitly ensure that the attention weights are informative for predicting the identity of the person in the input image, and may consequently introduce noisy information from the input image. To address this issue, we propose a novel method termed Reduction of Information Bottleneck loss (RIB), motivated by the principle of the Information Bottleneck (IB). A novel distribution-free and efficient variational upper bound for the IB loss (IBB), which can be optimized by standard {SGD}, is derived and incorporated into the training loss of the RIB models. RIB is applied to DNNs with self-attention through a novel Differentiable Channel Selection Attention module (DCS-Attention), which selects informative channels to compute attention weights, leading to RIB-DCS. It is also integrated with existing channel attention modules to learn informative channel weights, leading to RIB-CA. Both RIB-DCS and RIB-CA are applied to fixed backbones and learnable backbones with Differentiable Neural Architecture Search (DNAS). Extensive experiments on multiple person Re-ID benchmarks show that RIB significantly improves prediction accuracy, including in occluded person Re-ID, by learning more discriminative and identity-informative features. The code of our work is available at https://github.com/Statistical-Deep-Learning/RIB-ReID.