Mask refinement method

[edwardnguyen1705]

Dear @lufficc , thank you for sharing your work.
As you said in other issue:
We first use traditional edge detection to extract coarse masks, and then use Salient Object Detection to refine the coarse masks.

As I know, there are two approaches to refining the coarse masks:

1. Traditional image processing: Grabcut https://dl.acm.org/doi/10.1145/1186562.1015720 and its extent densecut http://mmcheng.net/densecut/
2. Deep learning: https://github.com/AceCoooool/DSS-pytorch. This method requires images and their corresponding labels to train the model.

Could you let me know which method you used to refine the coarse masks?
If you used the 2nd approach as I mention above, then you must used the already refined masks to train the network?

[lufficc]

We use cv2.ximgproc.createStructuredEdgeDetection (https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO/-/blob/master/toolboxes/extract_mask.py#L95), which produces perfect mask for some images(fails on others images). Then these perfect masks are used as ground truth to train https://github.com/AceCoooool/DSS-pytorch.

The extracted masks can be download here: Google Drive