Triplet interactive attention network for cross-modality person re-identification

Abstract

Cross-modality (RGB-Infrared) person re-identification (ReID) has extreme superiority on low illumination ReID over conventional RGB-RGB matching due to its excellent capability on bridging cross-modality discrepancy. Previous works often focus on extracting modality-invariant global features, whereas the interaction among pairwise samples provides the most important clues for identifying persons. To explore the interactive clues among cross-modality pedestrian images, we propose a Triplet Interactive Attention Network (TIAN) to imitate the pairwise interactions and generate the attention scores for triplet samples. It can discover the fine-grained difference between pedestrian images and is optimized by successive attention losses to strengthen the feature learning capability of the backbone network. Meanwhile, the triplet interactive attention loss further alleviates the cross-modality discrepancy, based on the maximum mean discrepancy constraint. To demonstrate the effectiveness of our proposed TIAN, experiments on SYSU-MM01 and RegDB datasets show obvious superiority over popular cross-modality ReID methods, and the evaluation of main modules also reveals the novelties of our TIAN method.