Real-time Semantic Segmentation with Parallel Multiple Views Feature Augmentation

Real-time semantic segmentation is essential for many practical applications, which utilizes attention-based feature aggregation into lightweight structures to improve accuracy and efficiency. However, existing attention-based methods ignore 1) high-level and low-level feature augmentation guided by spatial information, and 2) low-level feature augmentation guided by semantic context, so that feature gaps between multi-level features and noise of low-level spatial details still exist. To address these problems, a new real-time semantic segmentation network, called MvFSeg, is proposed. In MvFSeg, parallel convolution with multiple depths is designed as a context head to generate and integrate multi-view features with larger receptive fields. Moreover, MvFSeg designs multiple views feature augmentation strategies that exploit spatial and semantic guidance for shallow and deep feature augmentation in an inter-layer and intra-layer manner. These strategies eliminate feature gaps between multi-level features, filter out the noise of spatial details, and provide spatial and semantic guidance for multi-level features. By combining multi-view features and augmented features from the lightweight networks with progressive dense aggregation structures, MvFSeg effectively captures invariance at various scales and generates high-quality segmentation results. Experiments conducted on Cityscapes and CamVid benchmark show that MvFSeg outperforms existing state-of-the-art methods.