Pyramid context learning for object detection

Abstract

Contextual information in complex scenarios is critical for accurate object detection. Existing state-of-the-art detectors have greatly improved detection performance with the use of contexts around objects. However, these detectors consider the local and global contexts separately, which limits the improvement in detection accuracy. In this paper, we propose a pyramid context learning module (PCL) for object detection, which makes full use of the feature context at different levels. Specifically, two operators, named aggregation and distribution, are designed to assemble and synthesize contextual information at different levels. In addition, a channel context learning operator is also used to capture the channel context. PCL is a universal module, so it can be easily integrated into most of the detection frameworks. To evaluate our PCL, we apply it into some popular detectors, e.g., SSD, Faster R-CNN and RetinaNet, and conduct extensive experiments on PASCAL VOC and MS COCO datasets. Experimental results show that PCL can produce competitive performance gains and significantly improve the baselines.