Try Harder: Hard Sample Generation and Learning for Clothes-Changing Person Re-ID

• URL: http://arxiv.org/abs/2507.11119v1
• Date: Tue, 15 Jul 2025 09:14:01 GMT
• Title: Try Harder: Hard Sample Generation and Learning for Clothes-Changing Person Re-ID
• Authors: Hankun Liu, Yujian Zhao, Guanglin Niu,
• Abstract summary: Hard samples pose a significant challenge in person re-identification (ReID) tasks.<n>Their inherent ambiguity or similarity, coupled with the lack of explicit definitions, makes them a fundamental bottleneck.<n>We propose a novel multimodal-guided Hard Sample Generation and Learning framework.
• Score: 4.256800812615341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hard samples pose a significant challenge in person re-identification (ReID) tasks, particularly in clothing-changing person Re-ID (CC-ReID). Their inherent ambiguity or similarity, coupled with the lack of explicit definitions, makes them a fundamental bottleneck. These issues not only limit the design of targeted learning strategies but also diminish the model's robustness under clothing or viewpoint changes. In this paper, we propose a novel multimodal-guided Hard Sample Generation and Learning (HSGL) framework, which is the first effort to unify textual and visual modalities to explicitly define, generate, and optimize hard samples within a unified paradigm. HSGL comprises two core components: (1) Dual-Granularity Hard Sample Generation (DGHSG), which leverages multimodal cues to synthesize semantically consistent samples, including both coarse- and fine-grained hard positives and negatives for effectively increasing the hardness and diversity of the training data. (2) Hard Sample Adaptive Learning (HSAL), which introduces a hardness-aware optimization strategy that adjusts feature distances based on textual semantic labels, encouraging the separation of hard positives and drawing hard negatives closer in the embedding space to enhance the model's discriminative capability and robustness to hard samples. Extensive experiments on multiple CC-ReID benchmarks demonstrate the effectiveness of our approach and highlight the potential of multimodal-guided hard sample generation and learning for robust CC-ReID. Notably, HSAL significantly accelerates the convergence of the targeted learning procedure and achieves state-of-the-art performance on both PRCC and LTCC datasets. The code is available at https://github.com/undooo/TryHarder-ACMMM25.

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