{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BDHMJ4NL3X5CKOXCXGBQDCP4R5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9fed4f853d6ee17aee984f65a1ed8d8b4b8e0c80a5a70bfd580a833ba69f9265","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-28T18:59:50Z","title_canon_sha256":"e082ab73bce0280e45943f93418004dbc86f1e68fe3c25c4c114de4e0232d17c"},"schema_version":"1.0","source":{"id":"1711.10485","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.10485","created_at":"2026-05-18T00:29:21Z"},{"alias_kind":"arxiv_version","alias_value":"1711.10485v1","created_at":"2026-05-18T00:29:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10485","created_at":"2026-05-18T00:29:21Z"},{"alias_kind":"pith_short_12","alias_value":"BDHMJ4NL3X5C","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BDHMJ4NL3X5CKOXC","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BDHMJ4NL","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:d170ff2f750a52f2048507269a32a64f01899fb36a12afd71c084bb2e694d630","target":"graph","created_at":"2026-05-18T00:29:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous s","authors_text":"Han Zhang, Pengchuan Zhang, Qiuyuan Huang, Tao Xu, Xiaodong He, Xiaolei Huang, Zhe Gan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-28T18:59:50Z","title":"AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10485","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bc547f6d0511ee8fe73b024d67d7a2121e47cc4ccbf454614af9158cb94e3bd9","target":"record","created_at":"2026-05-18T00:29:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9fed4f853d6ee17aee984f65a1ed8d8b4b8e0c80a5a70bfd580a833ba69f9265","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-28T18:59:50Z","title_canon_sha256":"e082ab73bce0280e45943f93418004dbc86f1e68fe3c25c4c114de4e0232d17c"},"schema_version":"1.0","source":{"id":"1711.10485","kind":"arxiv","version":1}},"canonical_sha256":"08cec4f1abddfa253ae2b9830189fc8f55fb5a01c52452b16f3b7775e26a2acf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"08cec4f1abddfa253ae2b9830189fc8f55fb5a01c52452b16f3b7775e26a2acf","first_computed_at":"2026-05-18T00:29:21.707464Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:21.707464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z/7iEziZT78CyHwplzX7fkQcXcdXjr2Q17UBOXDchTS3RtX0ENxqCqUA23rI7/Bdc4aRIkaK4JfQrA4wSsvnAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:21.708121Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.10485","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc547f6d0511ee8fe73b024d67d7a2121e47cc4ccbf454614af9158cb94e3bd9","sha256:d170ff2f750a52f2048507269a32a64f01899fb36a12afd71c084bb2e694d630"],"state_sha256":"17ab07810d898e3894e53a961f6502a09ac1a1386a2d8cfe9ce5c96fe76bbcf4"}