利用相机内相似性进行伪标签精炼的无监督行人重识别
Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
AI总结：
针对无监督行人重识别中跨相机域偏移导致伪标签噪声的问题，提出利用相机内相似性生成可靠局部簇并精炼跨相机伪标签的自步训练框架，性能达到最先进水平。
摘要：
无监督行人重识别（Re-ID）旨在不使用任何身份标签的情况下跨相机检索行人图像。大多数基于聚类的方法粗略地将图像特征划分为簇，忽视了不同相机间域偏移导致的特征分布噪声，从而造成不可避免的性能下降。为应对这一挑战，我们提出一种结合聚类相机内相似性的新型标签精炼框架。相机内特征分布更关注行人外观，标签也更可靠。我们分别进行相机内训练以获得每个相机中的局部簇，并用局部结果精炼跨相机簇。随后，我们以自步方式使用精炼后的可靠伪标签训练 Re-ID 模型。大量实验表明，所提方法超越了当前最先进性能。
英文摘要：
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance degradation. To address this challenge, we propose a novel label refinement framework with clustering intra-camera similarity. Intra-camera feature distribution pays more attention to the appearance of pedestrians and labels are more reliable. We conduct intra-camera training to get local clusters in each camera, respectively, and refine inter-camera clusters with local results. We hence train the Re-ID model with refined reliable pseudo labels in a self-paced way. Extensive experiments demonstrate that the proposed method surpasses state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
