- Abstract:
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We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation. Our approach is based on a cascaded architecture with feature-level long-range skip connections. The encoder incorporates the structure of ResNeXt's residual building blocks and adopts the strategy of repeating a building block that aggregates a set of transformations with the same topology. The decoder features a novel architecture, consisting of blocks, that (i) capture context...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
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(pdf, 1.1mb)
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- Publisher copy:
- 10.1109/cvpr.2018.00690
-
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- Publisher:
- Institute for Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2018-12-17
- DOI:
- Pubs id:
-
pubs:953152
- URN:
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uri:28896bb0-1090-4e14-9b6a-c9bc82d1a0f6
- UUID:
-
uuid:28896bb0-1090-4e14-9b6a-c9bc82d1a0f6
- Local pid:
- pubs:953152
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
- © 2018 IEEE. This paper was presented at the Conference on Computer Vision and Pattern Recognition (CVPR 2018). This is the accepted manuscript version of the article. The final version is available online from IEEE at: 10.1109/CVPR.2018.00690
Conference item
Dense decoder shortcut connections for single-pass semantic segmentation
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