Video-Level Language-Driven Video-Based Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2506.02439v1
• Date: Tue, 03 Jun 2025 04:49:08 GMT
• Title: Video-Level Language-Driven Video-Based Visible-Infrared Person Re-Identification
• Authors: Shuang Li, Jiaxu Leng, Changjiang Kuang, Mingpi Tan, Xinbo Gao,
• Abstract summary: Video-based Visible-basedInfrared Person Re-Identification (VVIReID) aims to match pedestrian sequences across modalities by extracting modality-in sequence-level features.<n>A framework, video-level language-driven VVI-ReID (VLD), consists of two core modules: inmodality language (IMLP) and spatialtemporal aggregation.
• Score: 47.40091830500585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video-based Visible-Infrared Person Re-Identification (VVI-ReID) aims to match pedestrian sequences across modalities by extracting modality-invariant sequence-level features. As a high-level semantic representation, language provides a consistent description of pedestrian characteristics in both infrared and visible modalities. Leveraging the Contrastive Language-Image Pre-training (CLIP) model to generate video-level language prompts and guide the learning of modality-invariant sequence-level features is theoretically feasible. However, the challenge of generating and utilizing modality-shared video-level language prompts to address modality gaps remains a critical problem. To address this problem, we propose a simple yet powerful framework, video-level language-driven VVI-ReID (VLD), which consists of two core modules: invariant-modality language prompting (IMLP) and spatial-temporal prompting (STP). IMLP employs a joint fine-tuning strategy for the visual encoder and the prompt learner to effectively generate modality-shared text prompts and align them with visual features from different modalities in CLIP's multimodal space, thereby mitigating modality differences. Additionally, STP models spatiotemporal information through two submodules, the spatial-temporal hub (STH) and spatial-temporal aggregation (STA), which further enhance IMLP by incorporating spatiotemporal information into text prompts. The STH aggregates and diffuses spatiotemporal information into the [CLS] token of each frame across the vision transformer (ViT) layers, whereas STA introduces dedicated identity-level loss and specialized multihead attention to ensure that the STH focuses on identity-relevant spatiotemporal feature aggregation. The VLD framework achieves state-of-the-art results on two VVI-ReID benchmarks. The code will be released at https://github.com/Visuang/VLD.

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