Joint generative and camera-aware clustering for unsupervised domain adaptation on person re-identification

Existing supervised person re-identification (Re-ID) methods demonstrate excellent performance. However, their performances suffer degradation when tested on an unseen different distributed domain. Generative adversarial network-based (GAN-based) and clustering- or pseudolabel-based methods are proposed to alleviate this problem. Due to the transfer scheme and the ignorance of correlations between the style-transferred and the original target images, the performance of GAN-based methods is unsatisfactory. We resolve these problems by jointly employing a generative strategy and performing camera-aware clustering for the target domain. Style-transferred images are generated from source cameras to target cameras, and then they are merged into the target domain selectively after exploiting their domain-specific discriminative information. To reduce the noise in generated images, we propose a domain-level boundary separation loss to group the transferred images and push them away from the original target images. The camera-level neighborhood-based clustering is proposed to learn well-clustered features in a camera-aware manner. Extensive experiments on two commonly used person Re-ID datasets demonstrate that our proposed method can achieve state-of-the-art performance.