{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JJDIBMIFBNBEMWTICYYKGDIXBG","short_pith_number":"pith:JJDIBMIF","canonical_record":{"source":{"id":"1902.10740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T19:25:52Z","cross_cats_sorted":[],"title_canon_sha256":"baa868c417fb2ae256039a27b4cabba83ec21731563aca71c8b5982281810daf","abstract_canon_sha256":"8c87fb9a520a3041fde6bdcea26a6ab6a31211fb1c31ec2ce7008808b9729e82"},"schema_version":"1.0"},"canonical_sha256":"4a4680b1050b42465a681630a30d17099fbbfca37885ae98c8f0875e8589786d","source":{"kind":"arxiv","id":"1902.10740","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.10740","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"arxiv_version","alias_value":"1902.10740v1","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10740","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"pith_short_12","alias_value":"JJDIBMIFBNBE","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JJDIBMIFBNBEMWTI","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JJDIBMIF","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JJDIBMIFBNBEMWTICYYKGDIXBG","target":"record","payload":{"canonical_record":{"source":{"id":"1902.10740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T19:25:52Z","cross_cats_sorted":[],"title_canon_sha256":"baa868c417fb2ae256039a27b4cabba83ec21731563aca71c8b5982281810daf","abstract_canon_sha256":"8c87fb9a520a3041fde6bdcea26a6ab6a31211fb1c31ec2ce7008808b9729e82"},"schema_version":"1.0"},"canonical_sha256":"4a4680b1050b42465a681630a30d17099fbbfca37885ae98c8f0875e8589786d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:29.841227Z","signature_b64":"5zf2OVDJcZ5ZPbSOcHS6qACcV4Ni4d8DnCs2gIK7S0nT46mzWaDorJcq5nabbmZ+XLvVXy/Y+XKVWNioIFwQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a4680b1050b42465a681630a30d17099fbbfca37885ae98c8f0875e8589786d","last_reissued_at":"2026-05-17T23:52:29.840754Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:29.840754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.10740","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O54/HyqBQhNyXH3FEDE1MGWTHrs8+HZf+JSpLjSLN8k7MCBF2p/cIhuxiDxV1O98WAtvaqk/w5aH24/ATYFAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:13:30.797114Z"},"content_sha256":"f0bb4814a0ff6814dfe1c9b7d7b2b58ed9b39a2a6be33e96789bdcd8239efeec","schema_version":"1.0","event_id":"sha256:f0bb4814a0ff6814dfe1c9b7d7b2b58ed9b39a2a6be33e96789bdcd8239efeec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JJDIBMIFBNBEMWTICYYKGDIXBG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Object-driven Text-to-Image Synthesis via Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianfeng Gao, Lei Zhang, Pengchuan Zhang, Qiuyuan Huang, Siwei Lyu, Wenbo Li, Xiaodong He","submitted_at":"2019-02-27T19:25:52Z","abstract_excerpt":"In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes. Following the two-step (layout-image) generation process, a novel object-driven attentive image generator is proposed to synthesize salient objects by paying attention to the most relevant words in the text description and the pre-generated semantic layout. In addition, a new Fast R-CNN based object-wise discriminator is proposed to provide rich object-wise discrimination signals on whether the synthesized object matches the text de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10740","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LRgagK2EJ0t5I2YAuBpTRVs1N090tUfWYzimIVItUzh36iZ+Rp4n8gOpPdLHvrIcx1Wns1JI9UiXRQu+TuouAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:13:30.798042Z"},"content_sha256":"9053e1337c60c83010972d8628b2b45090449c3fa508dca115c00a42907d4b3b","schema_version":"1.0","event_id":"sha256:9053e1337c60c83010972d8628b2b45090449c3fa508dca115c00a42907d4b3b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/bundle.json","state_url":"https://pith.science/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T14:13:30Z","links":{"resolver":"https://pith.science/pith/JJDIBMIFBNBEMWTICYYKGDIXBG","bundle":"https://pith.science/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/bundle.json","state":"https://pith.science/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JJDIBMIFBNBEMWTICYYKGDIXBG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JJDIBMIFBNBEMWTICYYKGDIXBG","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":"8c87fb9a520a3041fde6bdcea26a6ab6a31211fb1c31ec2ce7008808b9729e82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T19:25:52Z","title_canon_sha256":"baa868c417fb2ae256039a27b4cabba83ec21731563aca71c8b5982281810daf"},"schema_version":"1.0","source":{"id":"1902.10740","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.10740","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"arxiv_version","alias_value":"1902.10740v1","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10740","created_at":"2026-05-17T23:52:29Z"},{"alias_kind":"pith_short_12","alias_value":"JJDIBMIFBNBE","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JJDIBMIFBNBEMWTI","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JJDIBMIF","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:9053e1337c60c83010972d8628b2b45090449c3fa508dca115c00a42907d4b3b","target":"graph","created_at":"2026-05-17T23:52:29Z","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 Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes. Following the two-step (layout-image) generation process, a novel object-driven attentive image generator is proposed to synthesize salient objects by paying attention to the most relevant words in the text description and the pre-generated semantic layout. In addition, a new Fast R-CNN based object-wise discriminator is proposed to provide rich object-wise discrimination signals on whether the synthesized object matches the text de","authors_text":"Jianfeng Gao, Lei Zhang, Pengchuan Zhang, Qiuyuan Huang, Siwei Lyu, Wenbo Li, Xiaodong He","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T19:25:52Z","title":"Object-driven Text-to-Image Synthesis via Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10740","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:f0bb4814a0ff6814dfe1c9b7d7b2b58ed9b39a2a6be33e96789bdcd8239efeec","target":"record","created_at":"2026-05-17T23:52:29Z","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":"8c87fb9a520a3041fde6bdcea26a6ab6a31211fb1c31ec2ce7008808b9729e82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-27T19:25:52Z","title_canon_sha256":"baa868c417fb2ae256039a27b4cabba83ec21731563aca71c8b5982281810daf"},"schema_version":"1.0","source":{"id":"1902.10740","kind":"arxiv","version":1}},"canonical_sha256":"4a4680b1050b42465a681630a30d17099fbbfca37885ae98c8f0875e8589786d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a4680b1050b42465a681630a30d17099fbbfca37885ae98c8f0875e8589786d","first_computed_at":"2026-05-17T23:52:29.840754Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:29.840754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5zf2OVDJcZ5ZPbSOcHS6qACcV4Ni4d8DnCs2gIK7S0nT46mzWaDorJcq5nabbmZ+XLvVXy/Y+XKVWNioIFwQDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:29.841227Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.10740","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0bb4814a0ff6814dfe1c9b7d7b2b58ed9b39a2a6be33e96789bdcd8239efeec","sha256:9053e1337c60c83010972d8628b2b45090449c3fa508dca115c00a42907d4b3b"],"state_sha256":"acb8523b61d7c9e93c5dc9a9a05ad3342857d7aac53cb6e4e3a1f1f97521d134"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9f1nfRLB+1OcIwwPgkYoHFMdmnyI7gkAsJvMmEjHKhA4b+qkhTJNFSUUF0jf7Y5BvQZLam77VWSs6SL6pM44Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T14:13:30.801511Z","bundle_sha256":"53ddf39bede0531b7743779baa3d5be65c7c8919b224e51d344690305cfcacbc"}}