Interactive segmentation is a popular technique used in computer vision that enables humans to interactively add or remove regions of an image based on their understanding of the scene. One recent technique that has garnered attention in this area is EdgeFlow, which fully utilizes interactive information of user clicks with edge-guided flow.
What is Edge Guidance?
Edge guidance is the idea that interactive segmentation improves segmentation masks progressively with user clicks. As users click around the edges of an object or region, the interactive segmentation model is able to use this information to improve the segmentation mask. This is done through an edge mask scheme, which takes the object edges estimated from the previous iteration as prior information, instead of direct mask estimation.
The Coarse-to-Fine Network
EdgeFlow incorporates a coarse-to-fine network consisting of two parts:
- CoarseNet: The base segmentation model utilized in CoarseNet is HRNet-18+OCR, which has demonstrated impressive results in image segmentation tasks. To incorporate edge guidance, EdgeFlow appends an edge-guided flow to the model so that interactive information can be effectively utilized.
- FineNet: The second part of the architecture consists of three atrous convolution blocks used to refine the coarse masks generated by CoarseNet.
The Benefits of EdgeFlow
One of the key benefits of EdgeFlow is that it enables users to interactively improve segmentation masks without significantly increasing the computational cost. This is because EdgewFlow allows for the re-use of prior edge information, which can be effectively integrated as users iteratively add more information. This approach can significantly reduce the cost of mask estimation and improve the accuracy of segmentation.
EdgeFlow is a promising technique for interactive segmentation that effectively incorporates edge guidance to improve the accuracy and computational efficiency of mask estimation. By leveraging prior knowledge and iterative improvements from user input, EdgeFlow represents a step forward in the development of interactive segmentation techniques that can more effectively incorporate human judgement.
