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.
Abstract (translated)
人员再识别（Re-ID）在实际的监控系统中面临若干挑战，包括因穿着变化（CCReID）和持续学习的需求（LReID）。以往的方法要么开发专门针对单一应用场景的模型，这通常是基于相同衣物设置的情况；要么将穿着变化视为一个独立的问题来处理。在这项工作中，我们将介绍LReID-Hybrid任务，旨在开发一种能够在连续学习环境中同时适应相同衣物与不同衣物情景的模型。 在从一项任务过渡到另一项任务时出现表示不匹配和遗忘问题是很重要的挑战，我们通过CMLReID（基于CLIP框架的方法）来解决这一问题。该方法由两个新颖的任务组成：(1) 具有上下文感知语义提示（Context-Aware Semantic Prompt, CASP）的任务，它可以生成自适应的提示，并将上下文信息纳入其中以对齐多粒度视觉线索与语义文本空间；以及 (2) 通过使用双路径学习器并结合衣物状态感知投影损失来产生稳健相同/不同衣物原型的自适应知识融合和投影（Adaptive Knowledge Fusion and Projection, AKFP）任务。实验在广泛的数据集上进行，结果表明CMLReID方法不仅超越了所有现有的最先进的技术，在面对服装变化的情况下还能保持强大的鲁棒性和泛化能力，并且即使是在复杂的序列学习过程中也是如此。
URL
https://arxiv.org/abs/2509.11247
