FMCNet+: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification

Abstract

For visible-infrared person re-identification (VI-ReID), current models that compensate modality-specific information strive to generate missing modality images from existing ones to bridge the cross-modality discrepancies. Despite that, those generated images often suffer from low qualities due to the significant modality gap and include interfering information, e.g., inconsistent colors, thus severely degrading the subsequent VI-ReID performance. Alternatively, we propose a feature-level modality compensation network, i.e., FMCNet+, for VI-ReID in this article as an improved version of our previous work (FMCNet). The core of FMCNet+ is to compensate for the missing modality-specific information at the feature level, rather than at the image level, enabling our model to generate more person-related and discriminative modality-specific features for VI-ReID. Concretely, FMCNet+ aims to progressively generate missing modality-specific features by fully exploring the relationships among single-modality features, modality-shared features, and modality-specific features, instead of directly generating them through a generative adversarial way as in the previous FMCNet. To this end, three modules, i.e., single-modality feature decomposition (SFD), modality characteristic dictionary learning (MCDL), and missing modality-specific feature compensation (MMFC), are incorporated in FMCNet+. Experimental results demonstrate the superiority of our proposed FMCNet+ over existing ones, especially for those that compensate for modality-specific information at the image level. Our intriguing findings highlight the necessity of feature-level modality compensation in VI-ReID. Our code and pre-trained models will be released on https://github.com/jssyzsfzy/FMCNet_series.