{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IIMXFYIMGGWM7D4LTCN6USK23R","short_pith_number":"pith:IIMXFYIM","schema_version":"1.0","canonical_sha256":"421972e10c31accf8f8b989bea495adc415c71197f087e805a4c045b357f05bc","source":{"kind":"arxiv","id":"1701.04658","version":2},"attestation_state":"computed","paper":{"title":"Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jordi Pont-Tuset, Kevis-Kokitsi Maninis, Luc Van Gool, Pablo Arbel\\'aez","submitted_at":"2017-01-17T13:04:33Z","abstract_excerpt":"We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour stren"},"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":"1701.04658","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-17T13:04:33Z","cross_cats_sorted":[],"title_canon_sha256":"58a8e9ec967ecbad76a75c16ef21f060ddb358e8b10729dc5cc1676a458a3296","abstract_canon_sha256":"61af16400168dd53022a1aab0fa7a711459d2d0f31d18d2cf2aba8c4ec46ac92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:25.734328Z","signature_b64":"dCByBnF46Dj4YsxsdAYtE7yyKTOIIl4LsBA4KVKUlMeouOjd3KzG8z+JWJmLUoi7pzeUp173y1D+mKnwg9wQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"421972e10c31accf8f8b989bea495adc415c71197f087e805a4c045b357f05bc","last_reissued_at":"2026-05-18T00:45:25.733853Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:25.733853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jordi Pont-Tuset, Kevis-Kokitsi Maninis, Luc Van Gool, Pablo Arbel\\'aez","submitted_at":"2017-01-17T13:04:33Z","abstract_excerpt":"We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour stren"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04658","kind":"arxiv","version":2},"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":"1701.04658","created_at":"2026-05-18T00:45:25.733926+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.04658v2","created_at":"2026-05-18T00:45:25.733926+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04658","created_at":"2026-05-18T00:45:25.733926+00:00"},{"alias_kind":"pith_short_12","alias_value":"IIMXFYIMGGWM","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"IIMXFYIMGGWM7D4L","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"IIMXFYIM","created_at":"2026-05-18T12:31:21.493067+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/IIMXFYIMGGWM7D4LTCN6USK23R","json":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R.json","graph_json":"https://pith.science/api/pith-number/IIMXFYIMGGWM7D4LTCN6USK23R/graph.json","events_json":"https://pith.science/api/pith-number/IIMXFYIMGGWM7D4LTCN6USK23R/events.json","paper":"https://pith.science/paper/IIMXFYIM"},"agent_actions":{"view_html":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R","download_json":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R.json","view_paper":"https://pith.science/paper/IIMXFYIM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.04658&json=true","fetch_graph":"https://pith.science/api/pith-number/IIMXFYIMGGWM7D4LTCN6USK23R/graph.json","fetch_events":"https://pith.science/api/pith-number/IIMXFYIMGGWM7D4LTCN6USK23R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R/action/storage_attestation","attest_author":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R/action/author_attestation","sign_citation":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R/action/citation_signature","submit_replication":"https://pith.science/pith/IIMXFYIMGGWM7D4LTCN6USK23R/action/replication_record"}},"created_at":"2026-05-18T00:45:25.733926+00:00","updated_at":"2026-05-18T00:45:25.733926+00:00"}