{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:COKWCYV5KKK4WQAAQNPVWOSGOJ","short_pith_number":"pith:COKWCYV5","canonical_record":{"source":{"id":"2310.15985","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-24T16:36:51Z","cross_cats_sorted":[],"title_canon_sha256":"5fe55744e2893109e779e9155fd6b28b62ac498883ac2ce76b97a36218030611","abstract_canon_sha256":"fb3388cf0d573d5bd542c44ea801502ac84a92ac00739683ec1dcb6d281d3eb4"},"schema_version":"1.0"},"canonical_sha256":"13956162bd5295cb4000835f5b3a46724d632a1ad9eaf630a7929e844040ac68","source":{"kind":"arxiv","id":"2310.15985","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.15985","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2310.15985v1","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15985","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"COKWCYV5KKK4","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"COKWCYV5KKK4WQAA","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"COKWCYV5","created_at":"2026-07-05T07:04:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:COKWCYV5KKK4WQAAQNPVWOSGOJ","target":"record","payload":{"canonical_record":{"source":{"id":"2310.15985","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-24T16:36:51Z","cross_cats_sorted":[],"title_canon_sha256":"5fe55744e2893109e779e9155fd6b28b62ac498883ac2ce76b97a36218030611","abstract_canon_sha256":"fb3388cf0d573d5bd542c44ea801502ac84a92ac00739683ec1dcb6d281d3eb4"},"schema_version":"1.0"},"canonical_sha256":"13956162bd5295cb4000835f5b3a46724d632a1ad9eaf630a7929e844040ac68","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:33.352463Z","signature_b64":"NLuV4K+qajgKPCMBGHAg+DEMUgs+OAfCCJY78HBteoIldYiAUgFA5wpuJpKH6e9w6Aro9+j6AFd7iYdQAOXVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13956162bd5295cb4000835f5b3a46724d632a1ad9eaf630a7929e844040ac68","last_reissued_at":"2026-07-05T07:04:33.351976Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:33.351976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.15985","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":"2026-07-05T07:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GtQh/+rKCG/4awdo3fwm21ryGOrnc7OE9gjO/+XurE4AcKCaQ0IOvZEFHXDghFi59TczwHr/stWT33dfIJPuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T10:02:07.277435Z"},"content_sha256":"70ec18ab97acb138245128ed2b14e77ea42958cdd50246df1ba37eba7ad908a8","schema_version":"1.0","event_id":"sha256:70ec18ab97acb138245128ed2b14e77ea42958cdd50246df1ba37eba7ad908a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:COKWCYV5KKK4WQAAQNPVWOSGOJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abby Stylianou, Liyu Gong, Nathan Jacobs, Srikumar Sastry, Xin Xing, Zhexiao Xiong","submitted_at":"2023-10-24T16:36:51Z","abstract_excerpt":"This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with standard multi-class image classification, where the task is predicting a single label from many possible labels for an image. Single-Positive Multi-label Learning (SPML) specifically considers learning to predict multiple labels when there is only a single annotation per image in the training data. Multi-label learning is in many ways a more realistic task than single-label lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15985","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.15985/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":"2026-07-05T07:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QTA3hUMqY/cyv5czwx6Tzgv6ktMwwqfFdICGE5KZrizWMQk8VueSRn+Mq30EoeUxqX1/Za6xZ99INjHI1bggDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T10:02:07.277944Z"},"content_sha256":"892154c59c4df85186889a0a9f710df3dde8f25d523f61884d3b900fc678c86e","schema_version":"1.0","event_id":"sha256:892154c59c4df85186889a0a9f710df3dde8f25d523f61884d3b900fc678c86e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/bundle.json","state_url":"https://pith.science/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/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":"2026-08-12T10:02:07Z","links":{"resolver":"https://pith.science/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ","bundle":"https://pith.science/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/bundle.json","state":"https://pith.science/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/COKWCYV5KKK4WQAAQNPVWOSGOJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:COKWCYV5KKK4WQAAQNPVWOSGOJ","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":"fb3388cf0d573d5bd542c44ea801502ac84a92ac00739683ec1dcb6d281d3eb4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-24T16:36:51Z","title_canon_sha256":"5fe55744e2893109e779e9155fd6b28b62ac498883ac2ce76b97a36218030611"},"schema_version":"1.0","source":{"id":"2310.15985","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.15985","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2310.15985v1","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15985","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"COKWCYV5KKK4","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"COKWCYV5KKK4WQAA","created_at":"2026-07-05T07:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"COKWCYV5","created_at":"2026-07-05T07:04:33Z"}],"graph_snapshots":[{"event_id":"sha256:892154c59c4df85186889a0a9f710df3dde8f25d523f61884d3b900fc678c86e","target":"graph","created_at":"2026-07-05T07:04:33Z","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/2310.15985/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with standard multi-class image classification, where the task is predicting a single label from many possible labels for an image. Single-Positive Multi-label Learning (SPML) specifically considers learning to predict multiple labels when there is only a single annotation per image in the training data. Multi-label learning is in many ways a more realistic task than single-label lear","authors_text":"Abby Stylianou, Liyu Gong, Nathan Jacobs, Srikumar Sastry, Xin Xing, Zhexiao Xiong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-24T16:36:51Z","title":"Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15985","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":"sha256:70ec18ab97acb138245128ed2b14e77ea42958cdd50246df1ba37eba7ad908a8","target":"record","created_at":"2026-07-05T07:04:33Z","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":"fb3388cf0d573d5bd542c44ea801502ac84a92ac00739683ec1dcb6d281d3eb4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-24T16:36:51Z","title_canon_sha256":"5fe55744e2893109e779e9155fd6b28b62ac498883ac2ce76b97a36218030611"},"schema_version":"1.0","source":{"id":"2310.15985","kind":"arxiv","version":1}},"canonical_sha256":"13956162bd5295cb4000835f5b3a46724d632a1ad9eaf630a7929e844040ac68","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"13956162bd5295cb4000835f5b3a46724d632a1ad9eaf630a7929e844040ac68","first_computed_at":"2026-07-05T07:04:33.351976Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:04:33.351976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NLuV4K+qajgKPCMBGHAg+DEMUgs+OAfCCJY78HBteoIldYiAUgFA5wpuJpKH6e9w6Aro9+j6AFd7iYdQAOXVAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:04:33.352463Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.15985","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70ec18ab97acb138245128ed2b14e77ea42958cdd50246df1ba37eba7ad908a8","sha256:892154c59c4df85186889a0a9f710df3dde8f25d523f61884d3b900fc678c86e"],"state_sha256":"42f2031ad1e426f6707ae3f673960f4c8917fc8ec40452cfd3d9ff7bdc02908d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nv48JieV4CEKo/jo36XHP+rZ++EPG/pa3a2cF3mkmdeDHhkjk5CGSJWWeLyFbjJCyDiLX/JWQEvm51aisLVRAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T10:02:07.282836Z","bundle_sha256":"78936f95b4d7833dd81d4c5a324a46d624049d9b730f7b6a43b754d5ec90ef1f"}}