SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow

Understand

In this paper, we focus on exploring effective methods for faster and accurate semantic segmentation.

• A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation.
• Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective.
• Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn \textit{Semantic Flow} between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently.