Abstract
Long-term Person Re-Identification (LRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing, pose, and viewpoint. In this work, we propose CCPA: Contrastive Clothing and Pose Augmentation framework for LRe-ID. Beyond appearance, CCPA captures body shape information which is cloth-invariant using a Relation Graph Attention Network. Training a robust LRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current LRe-ID datasets. To address this, we perform clothing and pose transfer across identities to generate images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on LRe-ID datasets demonstrate the effectiveness of our CCPA framework.
Abstract (translated)
长期人物识别（LRe-ID）旨在在长时间内匹配单个个体，展示衣物、姿势和视角的差异。在这项工作中，我们提出了CCPA：对比性服装和姿势增强框架用于LRe-ID。除了外观，CCPA通过使用关系图注意力网络捕捉身体形状信息，这是 cloth-invariant 的。训练一个稳健的LRe-ID模型需要广泛的服装变化和昂贵的布料标注，这在当前的LRe-ID数据集中是缺乏的。为了解决这个问题，我们在个体之间进行服装和姿势转移，生成更多服装变化和穿着类似服装不同人物的图像。增强的批片图像作为我们提出的细粒度对比损失的输入，不仅监督重新识别模型在长期场景下学习具有区分性的个体嵌入，而且还确保同分布数据的生成。在LRe-ID数据集上的结果证明了我们的CCPA框架的有效性。
URL
https://arxiv.org/abs/2402.14454
