Dynamically Adaptive Instance Normalization and Attention-Aware Incremental Meta-Learning for Generalizable Person Re-identification
Tiantian Gong, Kaixiang Chen, Junsheng Wang, Liyan Zhang · 2023
Domain generalization person re-identification (DG-ReID) aims to train a generalizable model over several source domains that can perform well on unseen target domains, which makes the DG-ReID task challenging since the model is not allowed to access any target data during training. The classic meta-learning method, which simulates the train-test process of DG-ReID task, is a popular and effective way for DG-ReID. Nevertheless, the method still suffers from the unstable meta-optimization problem. We thus propose a novel Dynamically Adaptive Instance Normalization and Attention-Aware Incremental Meta-Learning (DAIML) optimization method to effectively address this issue. In addition, most existing DG-ReID studies generally utilize Instance Normalization (IN) to learn domain-irrelevant features for eliminating domain shift, which may lead to the removal of effective domain-specific discriminative information that is usually useful to enhance the discriminability of a particular source. Therefore, we propose a dynamically adaptive IN (DAIN) that can balance well the learning of domain-invariant representations and domain-specific discriminative features. Furthermore, we apply channel attention and spatial attention to the proposed DAIN for further improving the domain-irrelevant discriminative information. Extensive experiments demonstrate the effectiveness of our proposed method on several DG-ReID datasets.