Dual-Adversarial Representation Disentanglement for Visible Infrared Person Re-Identification

作者：Ziyu Wei, Xi Yang, Nannan Wang, Xinbo Gao · 发表于：IEEE Transactions on Information Forensics and Security · 年份：2023 · DOI：10.1109/tifs.2023.3344289 · 被引用次数：54 · 研究领域：Video Surveillance and Tracking Methods、Fire Detection and Safety Systems、Advanced Neural Network Applications

Heterogeneous pedestrian images are captured by visible and infrared cameras with different spectrums, which play an important role in night-time video surveillance. However, visible infrared person re-identification (VI-REID) is still a challenging problem due to the considerable cross-modality discrepancies. To extract modality-invariant features which are discriminative for the person identity, recent studies are inclined to regard modality-specific features as noise and discard them. Actually, the modality-specific characteristics containing background and color information are indispensable for learning modality-shared features. In this paper, we propose a novel Dual-Adversarial Representation Disentanglement (DARD) model to separate modality-specific features from tangled pedestrian representations and effectively learn the robust modality-invariant representations. Specifically, our method employs dual-adversarial learning, incorporating image-level channel exchange and feature-level magnitude change to introduce variations in modality-specific representations. This deliberate perturbation raises the learning difficulty for the model to learn modality-shared features. Simultaneously, to control the changing scope of modality-specific features, bi-constrained noise alleviation is introduced during adversarial learning, keeping the balance of feature generation and adversary. The proposed dual-adversarial learning methodology enhances the robustness against cross-modalit...