Semantic Segmentation Models

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.

Source: EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow


Paper Code Results Date Stars


Task Papers Share
Image Segmentation 1 33.33%
Interactive Segmentation 1 33.33%
Semantic Segmentation 1 33.33%