Intermediary-Guided Bidirectional Spatial-Temporal Aggregation Network for Video-Based Visible-Infrared Person Re-Identification

This work focuses on the task of Video-based Visible-Infrared Person Re-Identification, a promising technique for achieving 24-hour surveillance systems. Two main issues in this field are modality discrepancy mitigating and spatial-temporal information mining. In this work, we propose a novel method, named Intermediary-guided Bidirectional spatial-temporal Aggregation Network (IBAN), to address both issues at once. Specifically, IBAN is designed to learn modality-irrelevant features by leveraging the anaglyph data of pedestrian images to serve as the intermediary. Furthermore, a bidirectional spatial-temporal aggregation module is introduced to exploit the spatial-temporal information of video data, while mitigating the impact of noisy image frames. Finally, we design an Easy-sample-based loss to guide the final embedding space and further improve the model's generalization performance. Extensive experiments on Video-based Visible-Infrared benchmarks show that IBAN achieves promising results and outperforms the state-of-the-art ReID methods by a large margin, improving the rank-1/mAP by 1.29%/3.46% at the Infrared to Visible situation, and by 5.04%/3.27% at the Visible to Infrared situation. The source code of the proposed method will be released at https://github.com/lhf12278/IBAN.