Scale-Insensitive Object Detection via Attention Feature Pyramid Transformer Network

With the progress of deep learning, object detection has attracted great attention in computer vision community. For object detection task, one key challenge is that object scale usually varies in a large range, which may make the existing detectors fail in real applications. To address this problem, we propose a novel end-to-end Attention Feature Pyramid Transformer Network framework to learn the object detectors with multi-scale feature maps via a transformer encoder-decoder fashion. AFPN learns to aggregate pyramid feature maps with attention mechanisms. Specifically, transformer-based attention blocks are used to scan through each spatial location of feature maps in the same pyramid layers and update it by aggregating information from deep to shadow layers. Furthermore, inter-level feature aggregation and intra-level information attention are repeated to encode multi-scale and self-attention feature representation. The extensive experiments on challenging MS COCO object detection dataset demonstrate that the proposed AFPN outperforms its baseline methods, i.e., DETR and Faster R-CNN methods, and achieves the state-of-the-art results.