Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification

Abstract

Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast, multi-modal object ReID utilizes complementary information from diverse modalities, showing great potentials for practical applications. How-ever, previous methods may be easily affected by irrele-vant backgrounds and usually ignore the modality gaps. To address above issues, we propose a novel learning frame-work named EDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. We be-gin with a shared vision Transformer to extract tokenized features from different input modalities. Then, we intro-duce a Spatial-Frequency Token Selection (SFTS) module to adaptively select object-centric tokens with both spa-tial and frequency information. Afterwards, we employ a Hierarchical Masked Aggregation (HMA) module to fa-cilitate feature interactions within and across modalities. Finally, to further reduce the effect of backgrounds, we propose a Background Consistency Constraint (BCC) and an Object-Centric Feature Refinement (OCFR). They are formulated as two new loss functions, which improve the feature discrimination with background suppression. As a result, our framework can generate more discriminative features for multi-modal object ReID. Extensive ex-periments on three multi-modal ReID benchmarks verify the effectiveness of our methods. The code is available at https://github.com/924973292/EDITOR.