分离行人身份特征，提升换装后人脸识别准确率。
Discriminative Pedestrian Features and Gated Channel Attention for Clothes-Changing Person Re-Identification
- 通过人体解析分离身份相关特征，忽略衣物变化影响。
- 在PRCC和VC-Clothes数据集上分别达到64.8%和83.7%的Top-1准确率。
- 适合关注换装场景下行人重识别的研究者与应用开发者。
在公共安全与社会生活中，换装行人重识别（CC-ReID）任务日益重要。由于衣物变化导致外观剧烈变动，该任务面临严峻挑战。本文提出一种解耦特征提取方法，有效从行人图像中提取对衣物变化不敏感的判别性特征。该方法利用行人解析技术识别并保留与个体身份紧密相关的特征，同时忽略衣物属性的可变性。此外，引入门控通道注意力机制，动态调节网络关注点，帮助模型更高效学习并强化对身份识别至关重要的特征。在两个标准CC-ReID数据集上的大量实验验证了该方法的有效性，性能超越当前领先方案。在PRCC和VC-Clothes数据集上，换装场景下的Top-1准确率分别达到64.8%和83.7%。
原文摘要 · Abstract (English)
In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant. However, this task faces considerable challenges due to appearance changes caused by clothing alterations. Addressing this issue, this paper proposes an innovative method for disentangled feature extraction, effectively extracting discriminative features from pedestrian images that are invariant to clothing. This method leverages pedestrian parsing techniques to identify and retain features closely associated with individual identity while disregarding the variable nature of clothing attributes. Furthermore, this study introduces a gated channel attention mechanism, which, by adjusting the network's focus, aids the model in more effectively learning and emphasizing features critical for pedestrian identity recognition. Extensive experiments conducted on two standard CC-ReID datasets validate the effectiveness of the proposed approach, with performance surpassing current leading solutions. The Top-1 accuracy under clothing change scenarios on the PRCC and VC-Clothes datasets reached 64.8% and 83.7%, respectively.
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