{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-automatic-natural-image-matting","title":"Deep Automatic Natural Image Matting","arxiv_id":"2107.07235","date":"2021-07-15","proceeding":null,"authors":["Jizhizi Li","Jing Zhang","DaCheng Tao"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2107.07235v1","url_pdf":"https://arxiv.org/pdf/2107.07235v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-automatic-natural-image-matting","repo_url":"https://github.com/JizhiziLi/AIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-matting","task_name":"Image Matting"}],"methods":[],"datasets_introduced":[{"slug":"aim-500","name":"AIM-500","full_name":"Automatic Image Matting-500"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-matting-on-aim-500","task":"Image Matting","dataset":"AIM-500","model":"AIM-Net","rank_in_archive_order":2,"of":6,"metrics":{"Conn.":"43.18","Grad.":"33.05","MAD":"0.0262","MSE":"0.0161","SAD":"43.92"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.07235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07235"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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