Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
arXiv:2003.13328
Abstract
Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies, 2) presenting a novel building block with diverse spatial pooling as a core, and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play module in existing scene parsing networks. Extensive experiments on popular benchmarks (e.g., ADE20K and Cityscapes) demonstrate that our simple approach establishes new state-of-the-art results. Code is made available at https://github.com/Andrew-Qibin/SPNet.
arXiv commentPublished as a CVPR2020 paper
References in corpus (13)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Conditional Random Fields as Recurrent Neural Networks
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- OCNet: Object Context Network for Scene Parsing
- CCNet: Criss-Cross Attention for Semantic Segmentation
- Context Encoding for Semantic Segmentation
- Dual Graph Convolutional Network for Semantic Segmentation
- Learning a Discriminative Feature Network for Semantic Segmentation
- Global Aggregation then Local Distribution in Fully Convolutional Networks
- SPGNet: Semantic Prediction Guidance for Scene Parsing