论文标题
可口可乐：一个换衣服的人数据集用于重新识别
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
论文作者
论文摘要
近年来，人的重新识别（RE-ID）取得了巨大进展。 Market1501，Cuhk03和Dukemtmc等几个学术基准，在促进RE-ID研究方面发挥了重要作用。据我们所知，所有现有的基准都认为同一人将拥有相同的衣服。在实际情况下，一个人通常换衣服。为了解决换衣服的人重新问题，我们构建了一个新颖的大规模重新标准，名为“换衣服套件”（COCAS），该基准提供了与不同衣服相同身份的多个图像。可口可乐完全包含来自5,266人的62,382个身体图像。基于可口可乐，我们介绍了一个新人重新设置，以解决衣服的问题，其中查询既包含衣服模板，又包括一个穿着其他衣服的人。此外，我们提出了一个名为Biometric-Clothes网络（BC-NET）的两分支网络，该网络可以有效地整合我们设置下的重新ID的生物特征和衣服功能。实验表明，用衣服模板更换衣服的衣服是可行的。
Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates.
