Magic Tokens: Select Diverse Tokens for Multi-modal Object
  Re-Identification
Pingping Zhang Yuhao Wang Yang Liu Zhengzheng Tu Huchuan Lu
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. However,
previous methods may be easily affected by irrelevant backgrounds and usually
ignore the modality gaps. To address above issues, we propose a novel learning
framework named \textbf{EDITOR} to select diverse tokens from vision
Transformers for multi-modal object ReID. We begin with a shared vision
Transformer to extract tokenized features from different input modalities.
Then, we introduce a Spatial-Frequency Token Selection (SFTS) module to
adaptively select object-centric tokens with both spatial and frequency
information. Afterwards, we employ a Hierarchical Masked Aggregation (HMA)
module to facilitate 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
experiments on three multi-modal ReID benchmarks verify the effectiveness of
our methods. The code is available at https://github.com/924973292/EDITOR.