Papers › Deep Automatic Natural Image Matting
Deep Automatic Natural Image Matting
Jizhizi Li, Jing Zhang, DaCheng Tao
Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation. Simultaneously, the learned semantic features guide the matting network to focus on the transition areas via an attention mechanism. We also construct a test set AIM-500 that contains 500 diverse natural images covering all types along with manually labeled alpha mattes, making it feasible to benchmark the generalization ability of AIM models. Results of the experiments demonstrate that our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively. The source code and dataset are available at https://github.com/JizhiziLi/AIM.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Matting | AIM-500 | AIM-Net | Conn. | 43.18 | #2 of 6 | Archive leaderboard | report |
| Image Matting | AIM-500 | AIM-Net | Grad. | 33.05 | #2 of 6 | Archive leaderboard | report |
| Image Matting | AIM-500 | AIM-Net | MAD | 0.0262 | #2 of 6 | Archive leaderboard | report |
| Image Matting | AIM-500 | AIM-Net | MSE | 0.0161 | #2 of 6 | Archive leaderboard | report |
| Image Matting | AIM-500 | AIM-Net | SAD | 43.92 | #2 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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