Semantic Flow for Fast and Accurate Scene Parsing

Abstract

In this paper, we focus on designing effective method for fast and accurate scene parsing. 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, are either computation intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels, and broadcast high-level features to high resolution features effectively and efficiently. Furthermore, integrating our module to a common feature pyramid structure exhibits superior performance over other real-time methods even on light-weight backbone networks, such as ResNet-18. Extensive experiments are conducted on several challenging datasets, including Cityscapes, PASCAL Context, ADE20K and CamVid. Especially, our network is the first to achieve 80.4\% mIoU on Cityscapes with a frame rate of 26 FPS. The code is available at \url{https://github.com/lxtGH/SFSegNets}.

arXiv commentaccepted by ECCV 2020(oral)

References in corpus (9)

• Semi-Supervised Classification with Graph Convolutional Networks
• Rethinking Atrous Convolution for Semantic Image Segmentation
• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• OCNet: Object Context Network for Scene Parsing
• Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
• Unsupervised Learning of Depth and Ego-Motion from Video
• Dual Attention Network for Scene Segmentation
• Semantic Understanding of Scenes through the ADE20K Dataset
• SPGNet: Semantic Prediction Guidance for Scene Parsing

Cited by in corpus (5)

• Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
• Rethinking BiSeNet For Real-time Semantic Segmentation
• Global-Local Propagation Network for RGB-D Semantic Segmentation
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
• AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing