{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:5MHXJV6MQ6NCQGROFS2BSZSUJV","short_pith_number":"pith:5MHXJV6M","schema_version":"1.0","canonical_sha256":"eb0f74d7cc879a281a2e2cb41966544d49d2c6dcb7bdc20472d58b237bb61eda","source":{"kind":"arxiv","id":"1409.3964","version":7},"attestation_state":"computed","paper":{"title":"Self-taught Object Localization with Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alessandro Bergamo, Dragomir Anguelov, Lorenzo Torresani, Loris Bazzani","submitted_at":"2014-09-13T16:12:43Z","abstract_excerpt":"This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of the image. The masking out of a region that includes the object typically causes a significant drop in recognition score. This idea is embedded into an agglomerative clustering technique that generate"},"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":"1409.3964","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-13T16:12:43Z","cross_cats_sorted":[],"title_canon_sha256":"c54638159acc40fb6d93d46d3d2371b86b277a8d38ff56fd4a504b7ac1d509ba","abstract_canon_sha256":"b8b4662ac4dceed463a73f80e68db7dc3055ad4c2f7a4d826e04eb54141ec5a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:32.649198Z","signature_b64":"B4JpyRY9ja8mW9CFa8eS++VLgUAULhhpmMS5UQsb+F6K+5mAw3+TZvaTIncHMp3/IbVnzqMI31DLpsHrpE64Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb0f74d7cc879a281a2e2cb41966544d49d2c6dcb7bdc20472d58b237bb61eda","last_reissued_at":"2026-05-18T01:21:32.648593Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:32.648593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-taught Object Localization with Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alessandro Bergamo, Dragomir Anguelov, Lorenzo Torresani, Loris Bazzani","submitted_at":"2014-09-13T16:12:43Z","abstract_excerpt":"This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of the image. The masking out of a region that includes the object typically causes a significant drop in recognition score. This idea is embedded into an agglomerative clustering technique that generate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.3964","kind":"arxiv","version":7},"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":"1409.3964","created_at":"2026-05-18T01:21:32.648677+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.3964v7","created_at":"2026-05-18T01:21:32.648677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.3964","created_at":"2026-05-18T01:21:32.648677+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MHXJV6MQ6NC","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MHXJV6MQ6NCQGRO","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MHXJV6M","created_at":"2026-05-18T12:28:14.216126+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.11820","citing_title":"Learning Rich Representations For Structured Visual Prediction Tasks","ref_index":96,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV","json":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV.json","graph_json":"https://pith.science/api/pith-number/5MHXJV6MQ6NCQGROFS2BSZSUJV/graph.json","events_json":"https://pith.science/api/pith-number/5MHXJV6MQ6NCQGROFS2BSZSUJV/events.json","paper":"https://pith.science/paper/5MHXJV6M"},"agent_actions":{"view_html":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV","download_json":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV.json","view_paper":"https://pith.science/paper/5MHXJV6M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.3964&json=true","fetch_graph":"https://pith.science/api/pith-number/5MHXJV6MQ6NCQGROFS2BSZSUJV/graph.json","fetch_events":"https://pith.science/api/pith-number/5MHXJV6MQ6NCQGROFS2BSZSUJV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV/action/storage_attestation","attest_author":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV/action/author_attestation","sign_citation":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV/action/citation_signature","submit_replication":"https://pith.science/pith/5MHXJV6MQ6NCQGROFS2BSZSUJV/action/replication_record"}},"created_at":"2026-05-18T01:21:32.648677+00:00","updated_at":"2026-05-18T01:21:32.648677+00:00"}