{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SIWEAK5FI7IUVYKUIDJLMJ4VMC","short_pith_number":"pith:SIWEAK5F","schema_version":"1.0","canonical_sha256":"922c402ba547d14ae15440d2b6279560b33480677e23ea393574dbc205a24901","source":{"kind":"arxiv","id":"1612.04770","version":1},"attestation_state":"computed","paper":{"title":"Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Nikos Komodakis, Spyros Gidaris","submitted_at":"2016-12-14T18:54:33Z","abstract_excerpt":"Pixel wise image labeling is an interesting and challenging problem with great significance in the computer vision community. In order for a dense labeling algorithm to be able to achieve accurate and precise results, it has to consider the dependencies that exist in the joint space of both the input and the output variables. An implicit approach for modeling those dependencies is by training a deep neural network that, given as input an initial estimate of the output labels and the input image, it will be able to predict a new refined estimate for the labels. In this context, our work is conc"},"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":"1612.04770","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T18:54:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7bfa8be06c4a808a1fc4e652830739194fd23a370cec5642ba76c5d0d65694e4","abstract_canon_sha256":"ea66f826e6e6533001a0bd83472dad57491709b0132587db74e6c3f41220bbce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:58.664165Z","signature_b64":"3eB3qwWD+xbEbkcEzQM2ClH0S0cPJ0dzpsQHH75WfzXsVsThx7S+bIQn22FtZ2j/z9J0xrvTaNhwrjXWpv1ICw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"922c402ba547d14ae15440d2b6279560b33480677e23ea393574dbc205a24901","last_reissued_at":"2026-05-18T00:54:58.663507Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:58.663507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Nikos Komodakis, Spyros Gidaris","submitted_at":"2016-12-14T18:54:33Z","abstract_excerpt":"Pixel wise image labeling is an interesting and challenging problem with great significance in the computer vision community. In order for a dense labeling algorithm to be able to achieve accurate and precise results, it has to consider the dependencies that exist in the joint space of both the input and the output variables. An implicit approach for modeling those dependencies is by training a deep neural network that, given as input an initial estimate of the output labels and the input image, it will be able to predict a new refined estimate for the labels. In this context, our work is conc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.04770","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":"1612.04770","created_at":"2026-05-18T00:54:58.663617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.04770v1","created_at":"2026-05-18T00:54:58.663617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.04770","created_at":"2026-05-18T00:54:58.663617+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIWEAK5FI7IU","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIWEAK5FI7IUVYKU","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIWEAK5F","created_at":"2026-05-18T12:30:44.179134+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/SIWEAK5FI7IUVYKUIDJLMJ4VMC","json":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC.json","graph_json":"https://pith.science/api/pith-number/SIWEAK5FI7IUVYKUIDJLMJ4VMC/graph.json","events_json":"https://pith.science/api/pith-number/SIWEAK5FI7IUVYKUIDJLMJ4VMC/events.json","paper":"https://pith.science/paper/SIWEAK5F"},"agent_actions":{"view_html":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC","download_json":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC.json","view_paper":"https://pith.science/paper/SIWEAK5F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.04770&json=true","fetch_graph":"https://pith.science/api/pith-number/SIWEAK5FI7IUVYKUIDJLMJ4VMC/graph.json","fetch_events":"https://pith.science/api/pith-number/SIWEAK5FI7IUVYKUIDJLMJ4VMC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC/action/storage_attestation","attest_author":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC/action/author_attestation","sign_citation":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC/action/citation_signature","submit_replication":"https://pith.science/pith/SIWEAK5FI7IUVYKUIDJLMJ4VMC/action/replication_record"}},"created_at":"2026-05-18T00:54:58.663617+00:00","updated_at":"2026-05-18T00:54:58.663617+00:00"}