{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:OVXYJVJCFWFVRLL6PD33ZPTJF6","short_pith_number":"pith:OVXYJVJC","schema_version":"1.0","canonical_sha256":"756f84d5222d8b58ad7e78f7bcbe692fb52afdef730990d6a7f560207b610db8","source":{"kind":"arxiv","id":"1612.08843","version":4},"attestation_state":"computed","paper":{"title":"FastMask: Segment Multi-scale Object Candidates in One Shot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Fei Sha, Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao","submitted_at":"2016-12-28T10:24:42Z","abstract_excerpt":"Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances in object scales. In the paper, we present a novel segment proposal framework, namely FastMask, which takes advantage of hierarchical features in deep convolutional neural networks to segment multi-scale objects in one shot. Innovatively, we adapt segment proposal network into three different functional components (body, neck and head). We further propose a weight-shared residual neck module as well as a scale-toler"},"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":"1612.08843","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-28T10:24:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"963feabf3a8252f59dfa57f4edceec4b2c098ef06d239e3b2fb00dbc05b3e32d","abstract_canon_sha256":"8f8bf9d4635ebc9907f2f3910c7fd1e95450e33caebbb922fb254f049a21058a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:31.356307Z","signature_b64":"3rnZ0OyOLrSZRJD53yi4lxgiMXf7geC3C5xSLuGAcMMkzRzCFEM8KzEzh026elEfNweumrNY2ZTGmooFyyeoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"756f84d5222d8b58ad7e78f7bcbe692fb52afdef730990d6a7f560207b610db8","last_reissued_at":"2026-05-18T00:46:31.355865Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:31.355865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FastMask: Segment Multi-scale Object Candidates in One Shot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Fei Sha, Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao","submitted_at":"2016-12-28T10:24:42Z","abstract_excerpt":"Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances in object scales. In the paper, we present a novel segment proposal framework, namely FastMask, which takes advantage of hierarchical features in deep convolutional neural networks to segment multi-scale objects in one shot. Innovatively, we adapt segment proposal network into three different functional components (body, neck and head). We further propose a weight-shared residual neck module as well as a scale-toler"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.08843","kind":"arxiv","version":4},"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":"1612.08843","created_at":"2026-05-18T00:46:31.355938+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.08843v4","created_at":"2026-05-18T00:46:31.355938+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.08843","created_at":"2026-05-18T00:46:31.355938+00:00"},{"alias_kind":"pith_short_12","alias_value":"OVXYJVJCFWFV","created_at":"2026-05-18T12:30:36.002864+00:00"},{"alias_kind":"pith_short_16","alias_value":"OVXYJVJCFWFVRLL6","created_at":"2026-05-18T12:30:36.002864+00:00"},{"alias_kind":"pith_short_8","alias_value":"OVXYJVJC","created_at":"2026-05-18T12:30:36.002864+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/OVXYJVJCFWFVRLL6PD33ZPTJF6","json":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6.json","graph_json":"https://pith.science/api/pith-number/OVXYJVJCFWFVRLL6PD33ZPTJF6/graph.json","events_json":"https://pith.science/api/pith-number/OVXYJVJCFWFVRLL6PD33ZPTJF6/events.json","paper":"https://pith.science/paper/OVXYJVJC"},"agent_actions":{"view_html":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6","download_json":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6.json","view_paper":"https://pith.science/paper/OVXYJVJC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.08843&json=true","fetch_graph":"https://pith.science/api/pith-number/OVXYJVJCFWFVRLL6PD33ZPTJF6/graph.json","fetch_events":"https://pith.science/api/pith-number/OVXYJVJCFWFVRLL6PD33ZPTJF6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6/action/storage_attestation","attest_author":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6/action/author_attestation","sign_citation":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6/action/citation_signature","submit_replication":"https://pith.science/pith/OVXYJVJCFWFVRLL6PD33ZPTJF6/action/replication_record"}},"created_at":"2026-05-18T00:46:31.355938+00:00","updated_at":"2026-05-18T00:46:31.355938+00:00"}