Hao Chen

TL;DR: Li et al. as discussed by the authors proposed a mesh-based view generator to generate novel views of a person and proposed a view-invariant loss to facilitate contrastive learning between original and generated views.

TL;DR: This paper incorporates a Generative Adversarial Network and a contrastive learning module into one joint training framework, and proposes a mesh-based view generator that significantly outperforms state-of-the-art methods under both, fully unsupervised and unsuper supervised domain adaptive settings on several large scale ReID dat-sets.

TL;DR: Li et al. as mentioned in this paper proposed an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task.

TL;DR: In this article, asymmetric branches are proposed to extract features in different manners, which enhances the feature diversity in appearance signatures, and cross-branch supervision allows one branch to get supervision from the other branch, which transfers distinct knowledge and enhances the weight diversity between teacher and student networks.