Compact Position-Aware Attention Network for Image Semantic Segmentation

In intelligent multimedia security, automatic image semantic segmentation is a fundamental research, which facilitates to accurately recognizing important targets from multimedia data and performing subsequent security analysis. Most existing semantic segmentation methods have made remarkable progress via modeling interactions between image pixels based on fully convolutional networks (FCN). However, they neglect the fact that semantic features extracted by FCN have poor ability to represent original image details, which always makes it hard to attend true positive relevant information within adjacent regions in spatial position for interactions modeling based methods. To tackle above problem, we take position information into account and adaptively model position relevance between pixels for enhancing local consistent in segmentation results. We propose a novel compact position-aware attention network (CPANet), containing spatial augmented attention module and channel augmented attention module, to simultaneously learn semantic relevance and position relevance between image pixels in a mutually reinforced way. In spatial augmented module, we introduce relative height and width distance to model position relevance based on self-attention mechanism. In channel augmented module, we exploit bilinear pooling to model compact correlation between pixels at any position across any channels. Our proposed CPANet can mutually learn accurate position and semantic of image pixels in a compact manner for improving semantic segmentation performance. Experimental results demonstrate that our approach has achieved the state-of-the-art performance in Cityscapes dataset.