Clothes-changing person re-identification (Re-ID) aims at learning identity-relevant feature representations among clothing-changed persons. Currently, the state-of-the-art methods accomplish this task by using additional assistance (e.g., silhouettes, sketches, clothes labels, etc.) to explore identity-relevant information. However, humans do not require redundant assistance information to retrieve clothing-changed persons. It is commonly known that humans can recall targets they have seen before with a simple reminder. Inspired by human perception, we propose an association and forgetting learning (AFL) framework for clothes-changing person re-identification. Specifically, on the one hand, during the association learning process, the AFL framework constructs association factors for each identity to simulate the reminders found in human perception. Then, the original instances and the explored hardest positive instances are cross-correlated by the association factors to learn identity-relevant features. On the other hand, the model is forced to forget the identity-irrelevant features by the proposed forgetting learning module, which improves the intra-class compactness. Finally, we further propose a clustering relationship exploration (CRE) module to optimize the cluster distribution of clothes-changing instances, which enables AFL to also be effectively applied in unsupervised settings, improving the universal applicability of the model. Extensive experiment results obtained on clothes-changing person Re-ID datasets under supervised and unsupervised settings demonstrate the superiority of the proposed method over the existing state-of-the-art methods.

Unsupervised person re-identification (Re-ID) aims to learn discriminative representations for person retrieval from unlabeled data. Currently, state-of-the-art techniques accomplish this task by using instance contrastive learning, which contrasts the similarities of the instances in different views. However, existing contrastive methods only focus on the positive effects of inter-instance relationships, while neglecting the negative effects of intra-instance redundancy information. This redundancy information can generate invalid or spurious intra-class relationships during the instance contrasting process, which enlarges the intra-class gaps and increases the noisy pseudo-labels. To address this issue, we propose a discriminative identity-feature exploring and differential aware learning (DiDAL) framework to learn more discriminative intra-identity representations. Specifically, the DiDAL extracts intra-instance salient features by synthetic complementary attention, and further explores the discriminative identity features by modeling the relationship among these salient features based on graph neural networks. This strategy aims to reduce the intra-instance redundancy information. Moreover, DiDAL explores hard instances by leveraging the extracted intra-instance salient features, and matches an anchor with multiple hard positive instances to enhance the robustness of the model to noisy pseudo-labels. Extensive experiment results on two widely used person re-identification datasets and a vehicle re-identification dataset demonstrate the superiority of the proposed method compared with existing state-of-the-art methods.

We propose an effective scheme of the deep learning method for high-order nonlinear soliton equations and explore the influence of activation functions on the calculation results for higher-order nonlinear soliton equations. The physics-informed neural networks approximate the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equations, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg–de Vries equation. The results show that the deep learning method can be used to solve high-order nonlinear soliton equations and reveal the interaction between solitons.