{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3SAKOZ73PGRZZA3TFPJCD226VI","short_pith_number":"pith:3SAKOZ73","schema_version":"1.0","canonical_sha256":"dc80a767fb79a39c83732bd221eb5eaa269e3fbd77cc179a0bfefd7d9768b44c","source":{"kind":"arxiv","id":"2304.04672","version":1},"attestation_state":"computed","paper":{"title":"Deep Image Matting: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Jing Zhang, Jizhizi Li","submitted_at":"2023-04-10T15:48:55Z","abstract_excerpt":"Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: aux"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.04672","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-10T15:48:55Z","cross_cats_sorted":[],"title_canon_sha256":"5db07385422d1e24294844d7d022c5a7ec530554fbf490b5cd8c85526ec11b55","abstract_canon_sha256":"4a400a5eb9fa9411d5b0eb7c040d268e441df762b590f86eeb3a8dde1583c2a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:59:28.854421Z","signature_b64":"JUa7gszJ7D4+7aP6CslOrjfUSItOQzYsgHFm2dnvNrzKI62HET9apoftd5ML1FrwY40Asn+h1wps2z3CGLw8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc80a767fb79a39c83732bd221eb5eaa269e3fbd77cc179a0bfefd7d9768b44c","last_reissued_at":"2026-07-05T05:59:28.853996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:59:28.853996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Image Matting: A Comprehensive Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Jing Zhang, Jizhizi Li","submitted_at":"2023-04-10T15:48:55Z","abstract_excerpt":"Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: aux"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.04672","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.04672/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.04672","created_at":"2026-07-05T05:59:28.854052+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.04672v1","created_at":"2026-07-05T05:59:28.854052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.04672","created_at":"2026-07-05T05:59:28.854052+00:00"},{"alias_kind":"pith_short_12","alias_value":"3SAKOZ73PGRZ","created_at":"2026-07-05T05:59:28.854052+00:00"},{"alias_kind":"pith_short_16","alias_value":"3SAKOZ73PGRZZA3T","created_at":"2026-07-05T05:59:28.854052+00:00"},{"alias_kind":"pith_short_8","alias_value":"3SAKOZ73","created_at":"2026-07-05T05:59:28.854052+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.05570","citing_title":"A Multi-Layer System for Ultra-High-Resolution Static 360-Degree Telepresence","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI","json":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI.json","graph_json":"https://pith.science/api/pith-number/3SAKOZ73PGRZZA3TFPJCD226VI/graph.json","events_json":"https://pith.science/api/pith-number/3SAKOZ73PGRZZA3TFPJCD226VI/events.json","paper":"https://pith.science/paper/3SAKOZ73"},"agent_actions":{"view_html":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI","download_json":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI.json","view_paper":"https://pith.science/paper/3SAKOZ73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.04672&json=true","fetch_graph":"https://pith.science/api/pith-number/3SAKOZ73PGRZZA3TFPJCD226VI/graph.json","fetch_events":"https://pith.science/api/pith-number/3SAKOZ73PGRZZA3TFPJCD226VI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI/action/storage_attestation","attest_author":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI/action/author_attestation","sign_citation":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI/action/citation_signature","submit_replication":"https://pith.science/pith/3SAKOZ73PGRZZA3TFPJCD226VI/action/replication_record"}},"created_at":"2026-07-05T05:59:28.854052+00:00","updated_at":"2026-07-05T05:59:28.854052+00:00"}