Context Learning Network for Object Detection
Jiaxu Leng, Ying Liu, Tianlin Zhang, Pei Quan · 2018
Current state-of-the-art detectors typically classify candidate proposals using their interior features. However, the valuable contexts are not fully exploited by existing detectors yet, which limits the detection performance. In this paper, we present a context learning network (CLN), which aims to capture pairwise relation between objects and global contexts of each object. The proposed CLN consists of two subnetworks: a multi-layer perceptron (MLP) with three layers and a convolutional neural network (ConvNet) with two layers. The MLP is first designed to capture the pairwise relation context. Pairwise relationship context is then gathered and concatenated to further learn the global contexts by the ConvNet. Finally, we obtain the desired context feature maps with rich contextual information that are useful for accurate object detection. The proposed CLN is lightweight and it is easy to embed in any existing networks for object detection. In this paper, we present a context-aware Faster-RCNN with the proposed CLN and conduct extensive experiments to evaluate its performance. Experimental results demonstrate that the context-aware Faster-RCNN achieves state-of-the-art performance with the 82.1%, 80.7% and 38.4%mAPs on VOC 2007, VOC 2012 and COCO datasets, respectively.