{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QMSUGDIGL6PFJ2ULZRNVQMN7O3","short_pith_number":"pith:QMSUGDIG","schema_version":"1.0","canonical_sha256":"8325430d065f9e54ea8bcc5b5831bf76c84971183c5c4579b34e8c38ce6e0930","source":{"kind":"arxiv","id":"1606.00625","version":1},"attestation_state":"computed","paper":{"title":"Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Wen Chen, Jianlong Fu, Tao Mei, Yu Liu","submitted_at":"2016-06-02T11:13:04Z","abstract_excerpt":"Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to \"forget\" the previous state when the visual sequence is incons"},"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":"1606.00625","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-02T11:13:04Z","cross_cats_sorted":[],"title_canon_sha256":"50b033c3afc6caec22ae7253ed3d5c78e2bbf44d9bd381e1b92d48e9ed053f9a","abstract_canon_sha256":"4182508d7c8d83bf754060a1ff4b52cf14a1d012d507c3b640b965db5997e45b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:04.035889Z","signature_b64":"7z4m4Q3DxdwF45mwZRRAgztlzMgrQZUfG69WHosxvq0ZuVcCLm2sO/21P567E/Yp863Z69J2MlKwLxDtvNH1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8325430d065f9e54ea8bcc5b5831bf76c84971183c5c4579b34e8c38ce6e0930","last_reissued_at":"2026-05-18T01:13:04.035518Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:04.035518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Wen Chen, Jianlong Fu, Tao Mei, Yu Liu","submitted_at":"2016-06-02T11:13:04Z","abstract_excerpt":"Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to \"forget\" the previous state when the visual sequence is incons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.00625","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":"1606.00625","created_at":"2026-05-18T01:13:04.035573+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.00625v1","created_at":"2026-05-18T01:13:04.035573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.00625","created_at":"2026-05-18T01:13:04.035573+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMSUGDIGL6PF","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMSUGDIGL6PFJ2UL","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMSUGDIG","created_at":"2026-05-18T12:30:39.010887+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/QMSUGDIGL6PFJ2ULZRNVQMN7O3","json":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3.json","graph_json":"https://pith.science/api/pith-number/QMSUGDIGL6PFJ2ULZRNVQMN7O3/graph.json","events_json":"https://pith.science/api/pith-number/QMSUGDIGL6PFJ2ULZRNVQMN7O3/events.json","paper":"https://pith.science/paper/QMSUGDIG"},"agent_actions":{"view_html":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3","download_json":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3.json","view_paper":"https://pith.science/paper/QMSUGDIG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.00625&json=true","fetch_graph":"https://pith.science/api/pith-number/QMSUGDIGL6PFJ2ULZRNVQMN7O3/graph.json","fetch_events":"https://pith.science/api/pith-number/QMSUGDIGL6PFJ2ULZRNVQMN7O3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3/action/storage_attestation","attest_author":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3/action/author_attestation","sign_citation":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3/action/citation_signature","submit_replication":"https://pith.science/pith/QMSUGDIGL6PFJ2ULZRNVQMN7O3/action/replication_record"}},"created_at":"2026-05-18T01:13:04.035573+00:00","updated_at":"2026-05-18T01:13:04.035573+00:00"}