Methods › Computer Vision › Semantic Segmentation Modules › Flow Alignment Module
Flow Alignment Module
Introduced by Xiangtai Li et al. in Semantic Flow for Fast and Accurate Scene Parsing
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Flow Alignment Module, or FAM, is a flow-based align module for scene parsing to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high resolution features effectively and efficiently. The concept of Semantic Flow is inspired from optical flow, which is widely used in video processing task to represent the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by relative motion. The authors postulate that the relatinship between two feature maps of arbitrary resolutions from the same image can also be represented with the “motion” of every pixel from one feature map to the other one. Once precise Semantic Flow is obtained, the network is able to propagate semantic features with minimal information loss.
In the FAM module, the transformed high-resolution feature map are combined with the low-resolution feature map to generate the semantic flow field, which is utilized to warp the low-resolution feature map to high-resolution feature map.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow 10 Jul 2022 · 1 repository · arXiv:2207.04415
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Fast and Accurate Scene Parsing via Bi-direction Alignment Networks 25 May 2021 · 1 repository · arXiv:2105.11651
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Semantic Flow for Fast and Accurate Scene Parsing 24 Feb 2020 · 6 repositories · arXiv:2002.10120Syntology ran 1 of 8 samples · 7 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Semantic Segmentation | 3 |
| Real-Time Semantic Segmentation | 2 |
| Scene Parsing | 2 |
| Optical Flow Estimation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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