{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:664A4AQHZ3YN3HOFLDXITTYGBU","short_pith_number":"pith:664A4AQH","schema_version":"1.0","canonical_sha256":"f7b80e0207cef0dd9dc558ee89cf060d24220e66e356e3dc0804ce37af0144ec","source":{"kind":"arxiv","id":"1906.12188","version":1},"attestation_state":"computed","paper":{"title":"A Deep Decoder Structure Based on WordEmbedding Regression for An Encoder-Decoder Based Model for Image Captioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Ahmad Asadi, Reza Safabakhsh","submitted_at":"2019-06-26T13:51:59Z","abstract_excerpt":"Generating textual descriptions for images has been an attractive problem for the computer vision and natural language processing researchers in recent years. Dozens of models based on deep learning have been proposed to solve this problem. The existing approaches are based on neural encoder-decoder structures equipped with the attention mechanism. These methods strive to train decoders to minimize the log likelihood of the next word in a sentence given the previous ones, which results in the sparsity of the output space. In this work, we propose a new approach to train decoders to regress the"},"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":"1906.12188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-26T13:51:59Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"a54734e362996b431e2259a7da1ba2e00b9b2ead7b97c974510bb4a1dd5e5d03","abstract_canon_sha256":"530d8b7d193e67ee81517b99361de126088c90c9ac6f65efd64a8e76173c9bce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:59.047532Z","signature_b64":"IUqd/EjN3s9l/U3cz/UpJgXf9VjPaM9NcpPd+kMX/E1cXK9KglYWEk4kc7UjyOL5jaKFuyfqli0mEAjhoBjpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7b80e0207cef0dd9dc558ee89cf060d24220e66e356e3dc0804ce37af0144ec","last_reissued_at":"2026-05-17T23:41:59.046964Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:59.046964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Decoder Structure Based on WordEmbedding Regression for An Encoder-Decoder Based Model for Image Captioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Ahmad Asadi, Reza Safabakhsh","submitted_at":"2019-06-26T13:51:59Z","abstract_excerpt":"Generating textual descriptions for images has been an attractive problem for the computer vision and natural language processing researchers in recent years. Dozens of models based on deep learning have been proposed to solve this problem. The existing approaches are based on neural encoder-decoder structures equipped with the attention mechanism. These methods strive to train decoders to minimize the log likelihood of the next word in a sentence given the previous ones, which results in the sparsity of the output space. In this work, we propose a new approach to train decoders to regress the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12188","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":"1906.12188","created_at":"2026-05-17T23:41:59.047050+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.12188v1","created_at":"2026-05-17T23:41:59.047050+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12188","created_at":"2026-05-17T23:41:59.047050+00:00"},{"alias_kind":"pith_short_12","alias_value":"664A4AQHZ3YN","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"664A4AQHZ3YN3HOF","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"664A4AQH","created_at":"2026-05-18T12:33:10.108867+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/664A4AQHZ3YN3HOFLDXITTYGBU","json":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU.json","graph_json":"https://pith.science/api/pith-number/664A4AQHZ3YN3HOFLDXITTYGBU/graph.json","events_json":"https://pith.science/api/pith-number/664A4AQHZ3YN3HOFLDXITTYGBU/events.json","paper":"https://pith.science/paper/664A4AQH"},"agent_actions":{"view_html":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU","download_json":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU.json","view_paper":"https://pith.science/paper/664A4AQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.12188&json=true","fetch_graph":"https://pith.science/api/pith-number/664A4AQHZ3YN3HOFLDXITTYGBU/graph.json","fetch_events":"https://pith.science/api/pith-number/664A4AQHZ3YN3HOFLDXITTYGBU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU/action/storage_attestation","attest_author":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU/action/author_attestation","sign_citation":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU/action/citation_signature","submit_replication":"https://pith.science/pith/664A4AQHZ3YN3HOFLDXITTYGBU/action/replication_record"}},"created_at":"2026-05-17T23:41:59.047050+00:00","updated_at":"2026-05-17T23:41:59.047050+00:00"}