{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WBY3YWKCZXAY5OURXAHNNDZCQX","short_pith_number":"pith:WBY3YWKC","schema_version":"1.0","canonical_sha256":"b071bc5942cdc18eba91b80ed68f2285e640afea195c2ca70e4d936887a88565","source":{"kind":"arxiv","id":"1803.09453","version":1},"attestation_state":"computed","paper":{"title":"CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baoyuan Wu, Linchao Bao, Wei Liu","submitted_at":"2018-03-26T07:56:43Z","abstract_excerpt":"This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatio-temporal Markov Random Field (MRF) model defined over pixels to handle this problem. Unlike conventional MRF models, the spatial dependencies among pixels in our model are encoded by a Convolutional Neural Network (CNN). Specifically, for a given object, the probability of a labeling to a set of spatially neighboring pixels can be predicted by a CNN trained for this specific object. As a result, higher-order, richer dependencies a"},"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":"1803.09453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-26T07:56:43Z","cross_cats_sorted":[],"title_canon_sha256":"36cc50c27fbfe3c1a72437da5d6b490497e46b7a13e1c57c2321c16ba4e08191","abstract_canon_sha256":"1532096d5990739b21cdafff9144f99df93f7c6ab0d7364b41a9be44c79a31b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:11.568052Z","signature_b64":"Vz3UF6RKnnN7KiQq4JdL8goiUB+veNlvX5BPC9R45s4VtPkjcB9vHWP5lrOXX5/8j6rSFNp+X8UwUQI/xNw7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b071bc5942cdc18eba91b80ed68f2285e640afea195c2ca70e4d936887a88565","last_reissued_at":"2026-05-18T00:20:11.567363Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:11.567363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baoyuan Wu, Linchao Bao, Wei Liu","submitted_at":"2018-03-26T07:56:43Z","abstract_excerpt":"This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatio-temporal Markov Random Field (MRF) model defined over pixels to handle this problem. Unlike conventional MRF models, the spatial dependencies among pixels in our model are encoded by a Convolutional Neural Network (CNN). Specifically, for a given object, the probability of a labeling to a set of spatially neighboring pixels can be predicted by a CNN trained for this specific object. As a result, higher-order, richer dependencies a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09453","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":""},"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":"1803.09453","created_at":"2026-05-18T00:20:11.567469+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.09453v1","created_at":"2026-05-18T00:20:11.567469+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09453","created_at":"2026-05-18T00:20:11.567469+00:00"},{"alias_kind":"pith_short_12","alias_value":"WBY3YWKCZXAY","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WBY3YWKCZXAY5OUR","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WBY3YWKC","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX","json":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX.json","graph_json":"https://pith.science/api/pith-number/WBY3YWKCZXAY5OURXAHNNDZCQX/graph.json","events_json":"https://pith.science/api/pith-number/WBY3YWKCZXAY5OURXAHNNDZCQX/events.json","paper":"https://pith.science/paper/WBY3YWKC"},"agent_actions":{"view_html":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX","download_json":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX.json","view_paper":"https://pith.science/paper/WBY3YWKC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.09453&json=true","fetch_graph":"https://pith.science/api/pith-number/WBY3YWKCZXAY5OURXAHNNDZCQX/graph.json","fetch_events":"https://pith.science/api/pith-number/WBY3YWKCZXAY5OURXAHNNDZCQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX/action/storage_attestation","attest_author":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX/action/author_attestation","sign_citation":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX/action/citation_signature","submit_replication":"https://pith.science/pith/WBY3YWKCZXAY5OURXAHNNDZCQX/action/replication_record"}},"created_at":"2026-05-18T00:20:11.567469+00:00","updated_at":"2026-05-18T00:20:11.567469+00:00"}