{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ORXZ6GFMKIBJ7JPH6JY4HUHOIK","short_pith_number":"pith:ORXZ6GFM","canonical_record":{"source":{"id":"2309.04965","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-10T08:55:24Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"3bd7fe577a2edae90ea7ac14f4ac483b3bf83c07b82b34a0211d2c98057b3509","abstract_canon_sha256":"c9b6fdca5d91052ea7eeea79dbe9f90bd3befecb3025c03ec398ca2dfbcd6c47"},"schema_version":"1.0"},"canonical_sha256":"746f9f18ac52029fa5e7f271c3d0ee42bce67af4262e3be4834842ab55ad575f","source":{"kind":"arxiv","id":"2309.04965","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.04965","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"2309.04965v2","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.04965","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"ORXZ6GFMKIBJ","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_16","alias_value":"ORXZ6GFMKIBJ7JPH","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_8","alias_value":"ORXZ6GFM","created_at":"2026-07-05T07:01:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ORXZ6GFMKIBJ7JPH6JY4HUHOIK","target":"record","payload":{"canonical_record":{"source":{"id":"2309.04965","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-10T08:55:24Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"3bd7fe577a2edae90ea7ac14f4ac483b3bf83c07b82b34a0211d2c98057b3509","abstract_canon_sha256":"c9b6fdca5d91052ea7eeea79dbe9f90bd3befecb3025c03ec398ca2dfbcd6c47"},"schema_version":"1.0"},"canonical_sha256":"746f9f18ac52029fa5e7f271c3d0ee42bce67af4262e3be4834842ab55ad575f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:25.555872Z","signature_b64":"ZHrzttAZ84yu2FE+/eh2EmhwDYvb2TAw/Ez5ivdKY/C8uq2tmTkQWAvPpi45BBIQMQSQyaUPP+KCEdKgbEAnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"746f9f18ac52029fa5e7f271c3d0ee42bce67af4262e3be4834842ab55ad575f","last_reissued_at":"2026-07-05T07:01:25.555404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:25.555404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.04965","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rcJVYVSUk8FlBJg7CFsnBdHwPpFKxQItj7Bue8FzkfQJw3IDRzIePA/5hfoSQrmIdEJ6NLdKKTPgCMfWrOQsDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T02:07:58.897954Z"},"content_sha256":"ee9ee19d4eac534e6d66a7fc9e651c55b3b1689d4d92b0ebbc732b414c0521e5","schema_version":"1.0","event_id":"sha256:ee9ee19d4eac534e6d66a7fc9e651c55b3b1689d4d92b0ebbc732b414c0521e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ORXZ6GFMKIBJ7JPH6JY4HUHOIK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Guisheng Liu, Haiyan Fu, Xiangyang Luo, Yanqing Guo, Yi Li, Zhengcong Fei","submitted_at":"2023-09-10T08:55:24Z","abstract_excerpt":"While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-word application of these systems. In this work, we propose a lightweight image captioning network in combination with continuous diffusion, called Prefix-diffusion. To achieve diversity, we design an efficient method that injects prefix image embeddings into the denoising process of the diffusion model. In order to reduce trainable parameters, we employ a pre-trained model to extract image features and further desig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.04965","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.04965/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G7MnVvSXrnQhbYVHuav3Q9LQDUZvc1QuEMh3zDBirQBX77f9BBNGzOpSlOK7o5+6gMFIJgqy8uQJ+2mHDKUvAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T02:07:58.900428Z"},"content_sha256":"72e300e1ec390507963e0800266e199c178c918444a4652931e10264f4c731f4","schema_version":"1.0","event_id":"sha256:72e300e1ec390507963e0800266e199c178c918444a4652931e10264f4c731f4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/bundle.json","state_url":"https://pith.science/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/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":"2026-08-14T02:07:58Z","links":{"resolver":"https://pith.science/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK","bundle":"https://pith.science/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/bundle.json","state":"https://pith.science/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ORXZ6GFMKIBJ7JPH6JY4HUHOIK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ORXZ6GFMKIBJ7JPH6JY4HUHOIK","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":"c9b6fdca5d91052ea7eeea79dbe9f90bd3befecb3025c03ec398ca2dfbcd6c47","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-10T08:55:24Z","title_canon_sha256":"3bd7fe577a2edae90ea7ac14f4ac483b3bf83c07b82b34a0211d2c98057b3509"},"schema_version":"1.0","source":{"id":"2309.04965","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.04965","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"2309.04965v2","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.04965","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"ORXZ6GFMKIBJ","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_16","alias_value":"ORXZ6GFMKIBJ7JPH","created_at":"2026-07-05T07:01:25Z"},{"alias_kind":"pith_short_8","alias_value":"ORXZ6GFM","created_at":"2026-07-05T07:01:25Z"}],"graph_snapshots":[{"event_id":"sha256:72e300e1ec390507963e0800266e199c178c918444a4652931e10264f4c731f4","target":"graph","created_at":"2026-07-05T07:01:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.04965/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-word application of these systems. In this work, we propose a lightweight image captioning network in combination with continuous diffusion, called Prefix-diffusion. To achieve diversity, we design an efficient method that injects prefix image embeddings into the denoising process of the diffusion model. In order to reduce trainable parameters, we employ a pre-trained model to extract image features and further desig","authors_text":"Guisheng Liu, Haiyan Fu, Xiangyang Luo, Yanqing Guo, Yi Li, Zhengcong Fei","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-10T08:55:24Z","title":"Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.04965","kind":"arxiv","version":2},"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:ee9ee19d4eac534e6d66a7fc9e651c55b3b1689d4d92b0ebbc732b414c0521e5","target":"record","created_at":"2026-07-05T07:01:25Z","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":"c9b6fdca5d91052ea7eeea79dbe9f90bd3befecb3025c03ec398ca2dfbcd6c47","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-10T08:55:24Z","title_canon_sha256":"3bd7fe577a2edae90ea7ac14f4ac483b3bf83c07b82b34a0211d2c98057b3509"},"schema_version":"1.0","source":{"id":"2309.04965","kind":"arxiv","version":2}},"canonical_sha256":"746f9f18ac52029fa5e7f271c3d0ee42bce67af4262e3be4834842ab55ad575f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"746f9f18ac52029fa5e7f271c3d0ee42bce67af4262e3be4834842ab55ad575f","first_computed_at":"2026-07-05T07:01:25.555404Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:25.555404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZHrzttAZ84yu2FE+/eh2EmhwDYvb2TAw/Ez5ivdKY/C8uq2tmTkQWAvPpi45BBIQMQSQyaUPP+KCEdKgbEAnDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:25.555872Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.04965","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee9ee19d4eac534e6d66a7fc9e651c55b3b1689d4d92b0ebbc732b414c0521e5","sha256:72e300e1ec390507963e0800266e199c178c918444a4652931e10264f4c731f4"],"state_sha256":"7ea7be94423d8c6c3d3ee053565bce386a07b5472d65cfd5a4f6dd664dcb6818"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vqHrKKJ+1PhvFZmLQyzDeAm1CdFc9V9GYZDoQcFWtJno7RCzy5cDznB4D39atZyFUmu0UvHSGrHR0I3vu2CpDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T02:07:58.908534Z","bundle_sha256":"0af7d4cf074407c022b387b5ec136535e6800b89f4633edcbad9eda66f71af38"}}