Internal–External Context Interaction Network for Person Re-Identification

Capturing discriminative cues with attention mechanisms is crucial for solving the high inter-class similarity problem of person re-identification (Re-ID). Self-attention (SA) learns its own contextual information within a single sample using self-affinity between elements, and some works have demonstrated its superiority in person Re-ID. However, SA weakens some subtle semantic cues and additional visual cues such as backpacks, which makes it difficult to distinguish similar-looking persons. In this paper, we propose an internal-external context interaction (IEI) attention mechanism, which aims to exploit the interaction of inter-sample latent context information and intra-sample local context information to enhance the feature representation of each element. The mechanism is able to capture subtle differences between persons and additional visual cues using inter-sample difference information and rich detail information within the element neighborhood, improving the ability to distinguish similar persons. Based on this mechanism, we propose an internal-external context interaction network (IEINet) for extracting discriminative features from multiple dimensions. In addition, to capture more discriminative information, we propose a region-diverse loss to constrain the network. Many experiments validate the effectiveness of our IEINet and demonstrate that our approach attains state-of-the-art performance on several large-scale person Re-ID datasets.