{"schema":"record/v1","id":"art_01M5NNY4YNRPHB2KGYV3BR4E0B","type":"article","revision":1,"etag":"\"r1.s0\"","title":"When an AI tool \"rotos\" a subject, is the result a segmentation mask or an alpha matte, and which metrics (J&F versus MAD/MSE/Grad/Conn/dtSSD) apply to each?","summary":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.","status":"active","body":{"text":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.\n\nCurator qualifications: Mask-vs-matte distinction and metric families are consistent with the sources read; the concept is well supported.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2408.00714v2","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/1704.00675v3","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/2108.11515v1","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/2501.14677v2","note":"Table 6; limitations; metrics"},{"url":"https://github.com/pq-yang/MatAnyone/blob/e5ddc534c1fff9bb9e54cf476095d29071b7cb4f/README.md","note":"Table 6; limitations; metrics"}]},"categories":["T11","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2408.00714v2","claim":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.","locator":"Table 6; limitations; metrics","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-3bda2dcf437cecca02a0e97a9bcf17f4b9921870/record.json#p-3bda2dcf437cecca02a0e97a9bcf17f4b9921870/record","import_batch_id":"pipe-76208761879f94a8feeb0d4dc0d226bb77faeaba","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-76208761879f94a8feeb0d4dc0d226bb77faeaba","original":{"body":{"text":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.\n\nCurator qualifications: Mask-vs-matte distinction and metric families are consistent with the sources read; the concept is well supported.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2408.00714v2","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/1704.00675v3","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/2108.11515v1","note":"Table 6; limitations; metrics"},{"url":"https://arxiv.org/abs/2501.14677v2","note":"Table 6; limitations; metrics"},{"url":"https://github.com/pq-yang/MatAnyone/blob/e5ddc534c1fff9bb9e54cf476095d29071b7cb4f/README.md","note":"Table 6; limitations; metrics"}]},"tags":[],"type":"article","links":[],"title":"When an AI tool \"rotos\" a subject, is the result a segmentation mask or an alpha matte, and which metrics (J&F versus MAD/MSE/Grad/Conn/dtSSD) apply to each?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2408.00714v2","claim":"Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.","locator":"Table 6; limitations; metrics","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T11","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":2,"topic_ids":["CAT:T11.05"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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