We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| JizhiziLi/GFM | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| build_bb | Ran | JizhiziLi/GFM/core/gfm.py code served (permissive licence) · get_code("615e143b7fb4a1e0") |
| conv3x3 | Ran | this paper's copy was not recorded; identical code first harvested from lixiaotong97/DSU pointer only · get_code("fac5364e2f53c6db") |
| conv_up_psp | Ran | JizhiziLi/GFM/core/gfm.py code served (permissive licence) · get_code("19d362704df07ba4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Extracting accurate foregrounds from natural images benefits many downstream applications such as film production and augmented reality. However, the furry characteristics and various appearance of the foregrounds, e.g., animal and portrait, challenge existing matting methods, which usually require extra user inputs such as trimap or scribbles. To resolve these problems, we study the distinct roles of semantics and details for image matting and decompose the task into two parallel sub-tasks: high-level semantic segmentation and low-level details matting. Specifically, we propose a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders to learn both tasks in a collaborative manner for end-to-end natural image matting. Besides, due to the limitation of available natural images in the matting task, previous methods typically adopt composite images for training and evaluation, which result in limited generalization ability on real-world images. In this paper, we investigate the domain gap issue between composite images and real-world images systematically by conducting comprehensive analyses of various discrepancies between the foreground and background images. We find that a carefully designed composition route RSSN that aims to reduce the discrepancies can lead to a better model with remarkable generalization ability. Furthermore, we provide a benchmark containing 2,000 high-resolution real-world animal images and 10,000 portrait images along with their manually labeled alpha mattes to serve as a test bed for evaluating matting model's generalization ability on real-world images. Comprehensive empirical studies have demonstrated that GFM outperforms state-of-the-art methods and effectively reduces the generalization error. The code and the datasets will be released at https://github.com/JizhiziLi/GFM.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.16188")
get_code_for_paper("2010.16188")
have("2010.16188")
Connect an agent — have() is free.