Eneralizable Multi Source Person Re Identification News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm. It consists of domain-aware mix-normalization (DMN) and domain-aware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization enhances the diversity of features during training from the normalization perspective of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To further promote the eff ....

Adaptation Models , Data Models , Omain Aware Mix Normalization , Eneralizable Multi Source Person Re Identification , Task Analysis , Training Data ,