Transformer-Based Feature Compensation Network for Aerial Photography Person and Ground Object Recognition

Abstract

Visible-infrared person re-identification (VI-ReID) aims at matching pedestrian images with the same identity between different modalities. Existing methods ignore the problems of detailed information loss and the difficulty in capturing global features during the feature extraction process. To solve these issues, we propose a Transformer-based Feature Compensation Network (TFCNet). Firstly, we design a Hierarchical Feature Aggregation (HFA) module, which recursively aggregates the hierarchical features to help the model preserve detailed information. Secondly, we design the Global Feature Compensation (GFC) module, which exploits Transformer’s ability to capture long-range dependencies in sequences to extract global features. Extensive results show that the rank-1/mAP of our method on the SYSU-MM01 and RegDB datasets reaches 60.87%/58.87% and 91.02%/75.06%, respectively, which is better than most existing excellent methods. Meanwhile, to demonstrate our method‘s transferability, we also conduct related experiments on two aerial photography datasets.