Context-Aware Hierarchical Feature Attention Network For Multi-Scale Object Detection

Abstract

Multi-scale object detection involves classification and regression assignments of objects with variable scales from an image. How to extract discriminative features is a key point for multi-scale object detection. Recent detectors simply fuse pyramidal features extracted from ConvNets, which does not take full advantage of useful features and drop out redundant features. To address this problem, we propose Context-Aware Hierarchical Feature Attention Network (CHFANet) to focus on effective multi-scale feature extraction for object detection. Based on single shot multibox detector (SSD) framework, the CHFANet consists of two components: the context-aware feature extraction (CFE) module to capture rich multi-scale context features and the hierarchical feature fusion (HFF) module followed with the channel-wise attention model to generate deeply fused attentive features. On the Pascal VOC benchmark, our CHFANet can achieve 82.6% mAP. Extensive experiments demonstrate that the CHFANet outperforms a lot of state-of-the-art object detectors in accuracy without any bells and whistles.