Context Prior for Scene Segmentation
arXiv:2004.01547
Abstract
Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and inter-class context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://git.io/ContextPrior.
arXiv commentAccepted to CVPR 2020. Code is available at https://git.io/ContextPrior
References in corpus (12)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- Fully Convolutional Networks for Semantic Segmentation
- Conditional Random Fields as Recurrent Neural Networks
- Squeeze-and-Excitation Networks
- ParseNet: Looking Wider to See Better
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- OCNet: Object Context Network for Scene Parsing
- Deep High-Resolution Representation Learning for Visual Recognition
- High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks