Methods › Computer Vision › Semantic Segmentation Models › EdgeFlow
EdgeFlow
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
EdgeFlow is an interactive segmentation architecture that fully utilizes interactive information of user clicks with edge-guided flow. Edge guidance is the idea that interactive segmentation improves segmentation masks progressively with user clicks. Based on user clicks, an edge mask scheme is used, which takes the object edges estimated from the previous iteration as prior information, instead of direct mask estimation (if the previous mask is used as input, poor segmentation results could result).
The architecture consists of a coarse-to-fine network including CoarseNet and FineNet. For CoarseNet, HRNet-18+OCR is utilized as the base segmentation model and the edge-guided flow is appended to deal with interactive information. For FineNet, three atrous convolution blocks are utilized to refine the coarse masks.
Papers archive 2025-07-28
1 shown of 1, 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.
-
EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow 20 Sep 2021 · 3 repositories · arXiv:2109.09406Syntology ran 2 of 5 samples · 3 unverified
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.
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections