See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

• URL: http://arxiv.org/abs/2412.01345v1
• Date: Mon, 02 Dec 2024 10:11:16 GMT
• Title: See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification
• Authors: Xiyu Han, Xian Zhong, Wenxin Huang, Xuemei Jia, Wenxuan Liu, Xiaohan Yu, Alex Chichung Kot,
• Abstract summary: Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing.<n>Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features.<n>We propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID.
• Score: 16.845045499676793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model's focus on distinctive ID characteristics. This integration enhances the model's discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github.

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