Review for NeurIPS paper: Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Weaknesses: - The main idea of this method is unified contrastive learning. However, the strategy of joint learning of source and target domain is not new although different methods implement with different losses (e.g., in [57,58]). It is also natural that the performance on source domain with joint learning of source and target domains is higher than finetuing with target data only. Besides, the form of non-parametric contrastive learning is widely used in general unsupervised visual representation learning methods (such as MoCo and SimCLR) and is not new in this method. It may meet with the current UDA benchmarks but the generality of this method based on such assumption is limited in those real-world practical application scenarios where no prior knowledge are available on target data. Existing methods which optimize source and target domains separately thus show more advantages in this aspect.