Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States

Robert Long; Rongxin Jiang; Mingrui Yan

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arXiv · 2509.11247

Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States

Robert Long·Rongxin Jiang·Mingrui Yan

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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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BibTeX RIS

Robert Long, Rongxin Jiang, Mingrui Yan. 2025-09-14. Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States. https://arxiv.org/abs/2509.11247

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