New CLIP-driven network enhances visible-infrared person re-identification

Researchers have developed CLIP4VI-ReID, a novel network designed for visible-infrared person re-identification. This system utilizes a CLIP semantic bridge to learn modality-shared representations, addressing the physical differences between natural and infrared images. The approach involves generating text semantics for visible images to align modalities, rectifying infrared feature embeddings with these semantics, and refining high-level semantic alignment to ensure id-related information is captured for accurate cross-modal matching. Experiments indicate that CLIP4VI-ReID outperforms existing state-of-the-art methods on relevant datasets. AI

IMPACT This research could improve the accuracy and efficiency of person re-identification systems across different visual modalities.

RANK_REASON This is a research paper detailing a novel method for a computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• CLIP4VI-ReID
• High-level Semantic Alignment
• Infrared Feature Embedding
• Text Semantic Generation
• Xizhan Gao

• paper
• model release

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