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 https://github.com/nengdong96/CSDN.

可见 - 红外人员再识别（VIReID）主要处理来自不同模态的人员图像之间的身份匹配，并通过融合高级语义与视觉特征来弥合模态差距。我们提出了一个基于 CLIP 的语义发现网络（CSDN），通过多模态学习的文本标记和集成文本特征来嵌入丰富的高级语义信息，从而促进了视觉特征的模态不变性。在多个常用基准测试上的实验评估证实了我们提出的 CSDN 方法的有效性和优越性。