FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

Abstract

Modern approaches for semantic segmentation usually employ dilated convolutions in the backbone to extract high-resolution feature maps, which brings heavy computation complexity and memory footprint. To replace the time and memory consuming dilated convolutions, we propose a novel joint upsampling module named Joint Pyramid Upsampling (JPU) by formulating the task of extracting high-resolution feature maps into a joint upsampling problem. With the proposed JPU, our method reduces the computation complexity by more than three times without performance loss. Experiments show that JPU is superior to other upsampling modules, which can be plugged into many existing approaches to reduce computation complexity and improve performance. By replacing dilated convolutions with the proposed JPU module, our method achieves the state-of-the-art performance in Pascal Context dataset (mIoU of 53.13%) and ADE20K dataset (final score of 0.5584) while running 3 times faster.

Code is available in https://github.com/wuhuikai/FastFCN

References in corpus (2)

• Rethinking Atrous Convolution for Semantic Image Segmentation
• Stacked Deconvolutional Network for Semantic Segmentation

Cited by in corpus (6)

• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
• Deep Convolutional Neural Networks with Spatial Regularization, Volume and Star-shape Priori for Image Segmentation
• Adaptive Context Encoding Module for Semantic Segmentation
• EfficientFCN: Holistically-guided Decoding for Semantic Segmentation
• Location-aware Upsampling for Semantic Segmentation
• GINet: Graph Interaction Network for Scene Parsing