{"url":"/sota/image-matting-on-amd","task":{"name":"Image Matting","url":"/task/image-matting","note":null},"dataset":{"name":"AMD","url":null},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Image Matting** is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.\r\n\r\n\r\n<span class=\"description-source\">Source: [Automatic Trimap Generation for Image Matting ](https://arxiv.org/abs/1707.00333)</span>\r\n\r\n<span class=\"description-source\">Image Source: [Real-Time High-Resolution Background Matting](https://arxiv.org/pdf/2012.07810v1.pdf)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["MAD","MSE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"MAD":null,"MSE":"lower"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MODNet+","metrics":{"MAD":"0.81","MSE":"0.0024"},"uses_additional_data":false,"paper_date":"2020-11-24","paper":"/paper/is-a-green-screen-really-necessary-for-real","paper_url":"https://arxiv.org/abs/2011.11961v4","paper_title":"MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition","code":"https://github.com/PaddlePaddle/PaddleSeg","n_code_links":9,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}