  The Visible-Infrared Person Re-identification (VI ReID) aims to match visible
and infrared images of the same pedestrians across non-overlapped camera views.
These two input modalities contain both invariant information, such as shape,
and modality-specific details, such as color. An ideal model should utilize
valuable information from both modalities during training for enhanced
representational capability. However, the gap caused by modality-specific
information poses substantial challenges for the VI ReID model to handle
distinct modality inputs simultaneously. To address this, we introduce the
Modality-aware and Instance-aware Visual Prompts (MIP) network in our work,
designed to effectively utilize both invariant and specific information for
identification. Specifically, our MIP model is built on the transformer
architecture. In this model, we have designed a series of modality-specific
prompts, which could enable our model to adapt to and make use of the specific
information inherent in different modality inputs, thereby reducing the
interference caused by the modality gap and achieving better identification.
Besides, we also employ each pedestrian feature to construct a group of
instance-specific prompts. These customized prompts are responsible for guiding
our model to adapt to each pedestrian instance dynamically, thereby capturing
identity-level discriminative clues for identification. Through extensive
experiments on SYSU-MM01 and RegDB datasets, the effectiveness of both our
designed modules is evaluated. Additionally, our proposed MIP performs better
than most state-of-the-art methods.
