Cross-Layer Feature Attention Module for Multi-scale Object Detection

Recent target detection networks adopt the attention mechanism for better feature abstraction. However, most of them draw feature attentions from merely one or two layers, failing to obtain consistent results for objects with different scales. In this paper, we propose a cross-layer feature attention module (CFAM) which can be plugged in any off-the-shelf architecture, and demonstrate that attentions obtained from multiple layers can further improve object detection. The proposed module consists of two components for cross-layer feature fusion and feature refinement, respectively. The former collects rich contextual cues by fusing the features from distinct layers, while the later calculates the cross-layer attention maps and applies them with the fused features. Experiments show the proposed module improves the detection rate by 2% against the baseline architecture, and outperforms recent state-of-the-art methods on the Pascal VOC benchmark.