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About this paper
Huikai Wu, Junge Zhang, Kaiqi Huang, Kongming Liang, Yizhou Yu
FastFCN presents an efficient semantic segmentation architecture that replaces computationally expensive dilated convolutions in the backbone with a Joint Pyramid Upsampling (JPU) module. This approach achieves a 2x-3x speedup while maintaining or improving segmentation accuracy on datasets like Pascal Context and ADE20K.
Key insights
- Dilated convolutions, while preserving resolution, introduce substantial computational overhead by forcing subsequent layers to operate on high-resolution feature maps.
- High-resolution features can be effectively recovered by reformulating the problem as a joint upsampling task, where low-resolution deep features are guided by higher-resolution shallow features.
- Challenging the necessity of dilated convolutions in backbone networks can lead to more efficient architectures without sacrificing accuracy for semantic segmentation.
Summary by alphaXiv.
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