  Visible-infrared person re-identification (VIReID) primarily deals with
matching identities across person images from different modalities. Due to the
modality gap between visible and infrared images, cross-modality identity
matching poses significant challenges. Recognizing that high-level semantics of
pedestrian appearance, such as gender, shape, and clothing style, remain
consistent across modalities, this paper intends to bridge the modality gap by
infusing visual features with high-level semantics. Given the capability of
CLIP to sense high-level semantic information corresponding to visual
representations, we explore the application of CLIP within the domain of
VIReID. Consequently, we propose a CLIP-Driven Semantic Discovery Network
(CSDN) that consists of Modality-specific Prompt Learner, Semantic Information
Integration (SII), and High-level Semantic Embedding (HSE). Specifically,
considering the diversity stemming from modality discrepancies in language
descriptions, we devise bimodal learnable text tokens to capture
modality-private semantic information for visible and infrared images,
respectively. Additionally, acknowledging the complementary nature of semantic
details across different modalities, we integrate text features from the
bimodal language descriptions to achieve comprehensive semantics. Finally, we
establish a connection between the integrated text features and the visual
features across modalities. This process embed rich high-level semantic
information into visual representations, thereby promoting the modality
invariance of visual representations. The effectiveness and superiority of our
proposed CSDN over existing methods have been substantiated through
experimental evaluations on multiple widely used benchmarks. The code will be
released at \url{https://github.com/nengdong96/CSDN}.
