{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:2I3OHTEFESGMZ5KOX574ZJBLRK","short_pith_number":"pith:2I3OHTEF","schema_version":"1.0","canonical_sha256":"d236e3cc85248cccf54ebf7fcca42b8aba2d96ebe9b7b9f11f51cb674d091901","source":{"kind":"arxiv","id":"1605.00052","version":1},"attestation_state":"computed","paper":{"title":"InterActive: Inter-Layer Activeness Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Jingdong Wang, Liang Zheng, Lingxi Xie, Qi Tian","submitted_at":"2016-04-30T02:28:11Z","abstract_excerpt":"An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying high-level context and improvin"},"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":"1605.00052","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-30T02:28:11Z","cross_cats_sorted":[],"title_canon_sha256":"b74d93e3cbdfb82fc58569c57a16832a8ad4f1cabe610e2d8b02659d9f5ccf6c","abstract_canon_sha256":"737eaf1900b8ff9c97dc999d352b91f89a2f73e593d307c04c78cd37b019009f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:56.975764Z","signature_b64":"dFm8/mhfFXi/4rXbOGvi9s4L6wwaSpV1qZoZbLc8iXovVSpKp7xPVA7LUCC6yVnIpFW2q/1nxwa3zyU/dshfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d236e3cc85248cccf54ebf7fcca42b8aba2d96ebe9b7b9f11f51cb674d091901","last_reissued_at":"2026-05-18T01:15:56.975175Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:56.975175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InterActive: Inter-Layer Activeness Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Yuille, Jingdong Wang, Liang Zheng, Lingxi Xie, Qi Tian","submitted_at":"2016-04-30T02:28:11Z","abstract_excerpt":"An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying high-level context and improvin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.00052","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":"1605.00052","created_at":"2026-05-18T01:15:56.975258+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.00052v1","created_at":"2026-05-18T01:15:56.975258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.00052","created_at":"2026-05-18T01:15:56.975258+00:00"},{"alias_kind":"pith_short_12","alias_value":"2I3OHTEFESGM","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"2I3OHTEFESGMZ5KO","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"2I3OHTEF","created_at":"2026-05-18T12:29:55.572404+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/2I3OHTEFESGMZ5KOX574ZJBLRK","json":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK.json","graph_json":"https://pith.science/api/pith-number/2I3OHTEFESGMZ5KOX574ZJBLRK/graph.json","events_json":"https://pith.science/api/pith-number/2I3OHTEFESGMZ5KOX574ZJBLRK/events.json","paper":"https://pith.science/paper/2I3OHTEF"},"agent_actions":{"view_html":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK","download_json":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK.json","view_paper":"https://pith.science/paper/2I3OHTEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.00052&json=true","fetch_graph":"https://pith.science/api/pith-number/2I3OHTEFESGMZ5KOX574ZJBLRK/graph.json","fetch_events":"https://pith.science/api/pith-number/2I3OHTEFESGMZ5KOX574ZJBLRK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK/action/storage_attestation","attest_author":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK/action/author_attestation","sign_citation":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK/action/citation_signature","submit_replication":"https://pith.science/pith/2I3OHTEFESGMZ5KOX574ZJBLRK/action/replication_record"}},"created_at":"2026-05-18T01:15:56.975258+00:00","updated_at":"2026-05-18T01:15:56.975258+00:00"}