提出新方法提升无监督行人重识别的鲁棒性与多样性。
TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identification
- 用令牌约束减少ViT图像块噪声影响。
- 多尺度记忆库挖掘异常样本潜力，提升特征一致性。
- 适合关注无监督重识别与模型鲁棒性的研究者。
本文提出基于视觉变换器（ViT）的令牌约束与多尺度记忆库（TCMM）方法，以解决无监督行人重识别中图像块噪声和特征不一致的问题。现有方法利用ViT特征生成伪标签与聚类原型，并通过对比学习训练模型，但ViT将图像分块嵌入，不可避免引入块级噪声，影响模型性能。同时，传统基于记忆库的对比学习受限于批次大小，易导致数据不一致；且伪标签方法常丢弃难聚类的异常样本，损失其潜在价值，限制模型多样性和鲁棒性。本文提出的令牌约束机制有效缓解块噪声对ViT结构的影响，多尺度记忆库则增强对异常样本的探索能力，维持特征一致性。实验表明，该系统在主流基准上达到领先性能。代码已开源：https://github.com/andy412510/TCMM。
原文摘要 · Abstract (English)
This paper proposes the ViT Token Constraint and Multi-scale Memory bank (TCMM) method to address the patch noises and feature inconsistency in unsupervised person re-identification works. Many excellent methods use ViT features to obtain pseudo labels and clustering prototypes, then train the model with contrastive learning. However, ViT processes images by performing patch embedding, which inevitably introduces noise in patches and may compromise the performance of the re-identification model. On the other hand, previous memory bank based contrastive methods may lead data inconsistency due to the limitation of batch size. Furthermore, existing pseudo label methods often discard outlier samples that are difficult to cluster. It sacrifices the potential value of outlier samples, leading to limited model diversity and robustness. This paper introduces the ViT Token Constraint to mitigate the damage caused by patch noises to the ViT architecture. The proposed Multi-scale Memory enhances the exploration of outlier samples and maintains feature consistency. Experimental results demonstrate that our system achieves state-of-the-art performance on common benchmarks. The project is available at \href{https://github.com/andy412510/TCMM}{https://github.com/andy412510/TCMM}.
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