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LVLM-Driven Attribute-Aware Modeling for Visible-Infrared Person Re-Identification
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Proposes an LVLM-driven attribute-aware modeling approach for visible-infrared person re-identification, as indicated by the title and partial abstract.
Problem
Visible-infrared person re-identification (VI-ReID) matches visible and infrared images of the same person; supervised methods need costly identity annotations, motivating unsupervised VI-ReID.
Approach
Uses a large vision-language model to drive attribute-aware modeling for cross-modality person matching; the abstract is truncated so the specific mechanism is not stated.
Limitations
- Full text unavailable; abstract truncated
Closest papers by meaning
- Empowering Visible-Infrared Person Re-Identification with Large Foundation Models (NeurIPS 2024 · similarity 0.91)
- High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-identification (AAAI 2024 · similarity 0.90)
- Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification (NeurIPS 2024 · similarity 0.90)
- Relieving Universal Label Noise for Unsupervised Visible-Infrared Person Re-Identification by Inferring from Neighbors (AAAI 2025 · similarity 0.90)
- TokenMatcher: Diverse Tokens Matching for Unsupervised Visible-Infrared Person Re-Identification (AAAI 2025 · similarity 0.90)
- Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification (CVPR 2024 · similarity 0.88)
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