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Multi-spectral object Re-identification (ReID) aims to retrieve specificobjects by leveraging complementary information from different image spectra.It delivers great advantages over traditional single-spectral ReID in complexvisual environment. However, the significant distribution gap among differentimage spectra poses great challenges for effective multi-spectral featurerepresentations. In addition, most of current Transformer-based ReID methodsonly utilize the global feature of class tokens to achieve the holisticretrieval, ignoring the local discriminative ones. To address the above issues,we step further to utilize all the tokens of Transformers and propose a cyclictoken permutation framework for multi-spectral object ReID, dubbled TOP-ReID.More specifically, we first deploy a multi-stream deep network based on visionTransformers to preserve distinct information from different image spectra.Then, we propose a Token Permutation Module (TPM) for cyclic multi-spectralfeature aggregation. It not only facilitates the spatial feature alignmentacross different image spectra, but also allows the class token of eachspectrum to perceive the local details of other spectra. Meanwhile, we proposea Complementary Reconstruction Module (CRM), which introduces dense token-levelreconstruction constraints to reduce the distribution gap across differentimage spectra. With the above modules, our proposed framework can generate morediscriminative multi-spectral features for robust object ReID. Extensiveexperiments on three ReID benchmarks (i.e., RGBNT201, RGBNT100 and MSVR310)verify the effectiveness of our methods. The code is available athttps://github.com/924973292/TOP-ReID.

TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation