Infrared-Visible Person Re-Identification via Multi-Modality Feature Fusion and Self-Distillation
Most existing cross-modality person re-identification methods mine modality-invariant features,while ignoring the discriminative features inherent to each modality.To fully utilize the inherent features in different modalities,an infrared-visible person re-identification method via multi-modality feature fusion and self-distillation is proposed.Firstly,an attention fusion mechanism based on a dual classifier is proposed.This mechanism assigns greater fusion weights to the self-owned features of each modality,while conversely as-signing lesser weights to the common features.This approach aims to obtain multi-modality fusion features that encapsulate the discriminative self-owned features of each modality.To enhance the robustness of network feature in adjusting to changes of pedestrian appearance,a memory storage is constructed to store the multi-view features of pedestrians.A parameter-free dynamic guidance strategy for self-distillation is also de-signed.This strategy aims to dynamically reinforce the multi-modality and multi-view reasoning capabilities of the network under the guidance of multi-modality fusion features and multi-view features.Finally,the network is able to infer the features of a pedestrian with different views of another modality from its single-modality image,thus improving the performance of the model for cross-modality person re-identification.Based on the PyTorch deep learning framework,comparative experiments are conducted with current main-stream methods on the public datasets SYSU-MM01 and RegDB.The results demonstrate that the proposed method achieves Rank-1 accuracies of 63.12％and 92.55％,respectively,along with mAP scores of 61.51％and 89.55％,re-spectively,which is superior to the comparison methods.
cross-modality person re-identificationfeature fusionattention mechanismmemory storage mecha-nismself-distillation
万方数据
维普
