{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KRXMRW4OCZ2HX6XQMRJ6TFW77E","short_pith_number":"pith:KRXMRW4O","canonical_record":{"source":{"id":"2303.13233","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-23T13:06:38Z","cross_cats_sorted":[],"title_canon_sha256":"364f27b9480f7ab50c73334912aaa5bc7caba3b4d9dd1658fe037e572fb19822","abstract_canon_sha256":"18a9dc3129205c86deb97dac582ec17716a461f3ebb34ecc99765b579a317983"},"schema_version":"1.0"},"canonical_sha256":"546ec8db8e16747bfaf06453e996dff9378faab5c9294b9058301fec07b71401","source":{"kind":"arxiv","id":"2303.13233","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.13233","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"2303.13233v2","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13233","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"KRXMRW4OCZ2H","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_16","alias_value":"KRXMRW4OCZ2HX6XQ","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_8","alias_value":"KRXMRW4O","created_at":"2026-07-05T06:42:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KRXMRW4OCZ2HX6XQMRJ6TFW77E","target":"record","payload":{"canonical_record":{"source":{"id":"2303.13233","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-23T13:06:38Z","cross_cats_sorted":[],"title_canon_sha256":"364f27b9480f7ab50c73334912aaa5bc7caba3b4d9dd1658fe037e572fb19822","abstract_canon_sha256":"18a9dc3129205c86deb97dac582ec17716a461f3ebb34ecc99765b579a317983"},"schema_version":"1.0"},"canonical_sha256":"546ec8db8e16747bfaf06453e996dff9378faab5c9294b9058301fec07b71401","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:42.965807Z","signature_b64":"FFUG+RN4Tzfz63wT1Tpe14A4xFSBTHb01dyEPkZR9J4g3BcS2M5tqOlh11G3r66GpzUtUNHeSWTg7e5xScYCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"546ec8db8e16747bfaf06453e996dff9378faab5c9294b9058301fec07b71401","last_reissued_at":"2026-07-05T06:42:42.965329Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:42.965329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.13233","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":"2026-07-05T06:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jFSviisOgCYTBLuyhL8YLJ4zhkOMzSxkrqsDmH5S0F/CXLCu+l4+K/bu3hDoDZggLbifI5qyMx1QiGPAaFZnBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T15:39:03.804467Z"},"content_sha256":"a95fe35d70d996370aeb9451d7498bf069eb1229f36033f0be99b84ac4c6c0c0","schema_version":"1.0","event_id":"sha256:a95fe35d70d996370aeb9451d7498bf069eb1229f36033f0be99b84ac4c6c0c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KRXMRW4OCZ2HX6XQMRJ6TFW77E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Juncheng Li, Qifan Yu, Siliang Tang, Wei Ji, Yueting Zhuang, Yu Wu","submitted_at":"2023-03-23T13:06:38Z","abstract_excerpt":"Scene Graph Generation (SGG) aims to extract <subject, predicate, object> relationships in images for vision understanding. Although recent works have made steady progress on SGG, they still suffer long-tail distribution issues that tail-predicates are more costly to train and hard to distinguish due to a small amount of annotated data compared to frequent predicates. Existing re-balancing strategies try to handle it via prior rules but are still confined to pre-defined conditions, which are not scalable for various models and datasets. In this paper, we propose a Cross-modal prediCate boostin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13233","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/2303.13233/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":"2026-07-05T06:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BzEbZh/HdRqnwwxXh3ayHyc05c4E42Xn/ZserFO4ftvzkdi+dncU/6x04A6SnPRwepKUR1vdrlHWQQTZHZOeCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T15:39:03.805124Z"},"content_sha256":"84f7904cee66a69a614ee7c6bb2fa26a09ee0d491513f14572b2534df55aa6db","schema_version":"1.0","event_id":"sha256:84f7904cee66a69a614ee7c6bb2fa26a09ee0d491513f14572b2534df55aa6db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/bundle.json","state_url":"https://pith.science/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/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":"2026-08-13T15:39:03Z","links":{"resolver":"https://pith.science/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E","bundle":"https://pith.science/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/bundle.json","state":"https://pith.science/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KRXMRW4OCZ2HX6XQMRJ6TFW77E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KRXMRW4OCZ2HX6XQMRJ6TFW77E","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":"18a9dc3129205c86deb97dac582ec17716a461f3ebb34ecc99765b579a317983","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-23T13:06:38Z","title_canon_sha256":"364f27b9480f7ab50c73334912aaa5bc7caba3b4d9dd1658fe037e572fb19822"},"schema_version":"1.0","source":{"id":"2303.13233","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.13233","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"2303.13233v2","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13233","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"KRXMRW4OCZ2H","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_16","alias_value":"KRXMRW4OCZ2HX6XQ","created_at":"2026-07-05T06:42:42Z"},{"alias_kind":"pith_short_8","alias_value":"KRXMRW4O","created_at":"2026-07-05T06:42:42Z"}],"graph_snapshots":[{"event_id":"sha256:84f7904cee66a69a614ee7c6bb2fa26a09ee0d491513f14572b2534df55aa6db","target":"graph","created_at":"2026-07-05T06:42:42Z","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/2303.13233/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scene Graph Generation (SGG) aims to extract <subject, predicate, object> relationships in images for vision understanding. Although recent works have made steady progress on SGG, they still suffer long-tail distribution issues that tail-predicates are more costly to train and hard to distinguish due to a small amount of annotated data compared to frequent predicates. Existing re-balancing strategies try to handle it via prior rules but are still confined to pre-defined conditions, which are not scalable for various models and datasets. In this paper, we propose a Cross-modal prediCate boostin","authors_text":"Juncheng Li, Qifan Yu, Siliang Tang, Wei Ji, Yueting Zhuang, Yu Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-23T13:06:38Z","title":"Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13233","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:a95fe35d70d996370aeb9451d7498bf069eb1229f36033f0be99b84ac4c6c0c0","target":"record","created_at":"2026-07-05T06:42:42Z","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":"18a9dc3129205c86deb97dac582ec17716a461f3ebb34ecc99765b579a317983","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-23T13:06:38Z","title_canon_sha256":"364f27b9480f7ab50c73334912aaa5bc7caba3b4d9dd1658fe037e572fb19822"},"schema_version":"1.0","source":{"id":"2303.13233","kind":"arxiv","version":2}},"canonical_sha256":"546ec8db8e16747bfaf06453e996dff9378faab5c9294b9058301fec07b71401","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"546ec8db8e16747bfaf06453e996dff9378faab5c9294b9058301fec07b71401","first_computed_at":"2026-07-05T06:42:42.965329Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:42:42.965329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FFUG+RN4Tzfz63wT1Tpe14A4xFSBTHb01dyEPkZR9J4g3BcS2M5tqOlh11G3r66GpzUtUNHeSWTg7e5xScYCCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:42:42.965807Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.13233","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a95fe35d70d996370aeb9451d7498bf069eb1229f36033f0be99b84ac4c6c0c0","sha256:84f7904cee66a69a614ee7c6bb2fa26a09ee0d491513f14572b2534df55aa6db"],"state_sha256":"bedc4182037fbca430b46da160062dc853acadac9af940ab29f97dcf85299ab0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rlQQUbDJTsHzCGRFsRLvSrLJ9SOQAOyzwxo2QxcWovRTpcthXlNxa7NihXRjM2Uwxeoimwc33gBsFpedJUXaDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T15:39:03.809639Z","bundle_sha256":"415d421f2c62fb57eb5ea471a058d1f84854b9c6d31fda6fc4f287b331fcd279"}}