Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States

• URL: http://arxiv.org/abs/2509.11247v1
• Date: Sun, 14 Sep 2025 12:46:39 GMT
• Title: Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States
• Authors: Robert Long, Rongxin Jiang, Mingrui Yan,
• Abstract summary: Person Re-Identification (ReID) has several challenges in real-world surveillance systems due to clothing changes (CCReID)<n>Previous existing methods either develop models specifically for one application or treat CCReID as its own sub-problem.<n>We will introduce the LReID-Hybrid task with the goal of developing a model to achieve both SC and CC while learning in a continual setting.
• Score: 2.6399783378460158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-Identification (ReID) has several challenges in real-world surveillance systems due to clothing changes (CCReID) and the need for maintaining continual learning (LReID). Previous existing methods either develop models specifically for one application, which is mostly a same-cloth (SC) setting or treat CCReID as its own separate sub-problem. In this work, we will introduce the LReID-Hybrid task with the goal of developing a model to achieve both SC and CC while learning in a continual setting. Mismatched representations and forgetting from one task to the next are significant issues, we address this with CMLReID, a CLIP-based framework composed of two novel tasks: (1) Context-Aware Semantic Prompt (CASP) that generates adaptive prompts, and also incorporates context to align richly multi-grained visual cues with semantic text space; and (2) Adaptive Knowledge Fusion and Projection (AKFP) which produces robust SC/CC prototypes through the use of a dual-path learner that aligns features with our Clothing-State-Aware Projection Loss. Experiments performed on a wide range of datasets and illustrate that CMLReID outperforms all state-of-the-art methods with strong robustness and generalization despite clothing variations and a sophisticated process of sequential learning.

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