  In Re-identification (ReID), recent advancements yield noteworthy progress in
both unimodal and cross-modal retrieval tasks. However, the challenge persists
in developing a unified framework that could effectively handle varying
multimodal data, including RGB, infrared, sketches, and textual information.
Additionally, the emergence of large-scale models shows promising performance
in various vision tasks but the foundation model in ReID is still blank. In
response to these challenges, a novel multimodal learning paradigm for ReID is
introduced, referred to as All-in-One (AIO), which harnesses a frozen
pre-trained big model as an encoder, enabling effective multimodal retrieval
without additional fine-tuning. The diverse multimodal data in AIO are
seamlessly tokenized into a unified space, allowing the modality-shared frozen
encoder to extract identity-consistent features comprehensively across all
modalities. Furthermore, a meticulously crafted ensemble of cross-modality
heads is designed to guide the learning trajectory. AIO is the \textbf{first}
framework to perform all-in-one ReID, encompassing four commonly used
modalities. Experiments on cross-modal and multimodal ReID reveal that AIO not
only adeptly handles various modal data but also excels in challenging
contexts, showcasing exceptional performance in zero-shot and domain
generalization scenarios.
