{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:Z3XR6EMENISDCM7J2M2IO4YJYI","short_pith_number":"pith:Z3XR6EME","canonical_record":{"source":{"id":"1806.08503","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-22T05:55:20Z","cross_cats_sorted":[],"title_canon_sha256":"2b0de6f087d8313a780c0f19b36690e8fce746093e67591dd6c30bae549449ce","abstract_canon_sha256":"36b32f8332dcb8c39ef1a85f842b175680361a2054d7e07bfa5d9c3bd65d008b"},"schema_version":"1.0"},"canonical_sha256":"ceef1f11846a243133e9d334877309c223779c629b481af07cc0d6f4849012ce","source":{"kind":"arxiv","id":"1806.08503","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.08503","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"arxiv_version","alias_value":"1806.08503v1","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.08503","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"pith_short_12","alias_value":"Z3XR6EMENISD","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Z3XR6EMENISDCM7J","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Z3XR6EME","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:Z3XR6EMENISDCM7J2M2IO4YJYI","target":"record","payload":{"canonical_record":{"source":{"id":"1806.08503","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-22T05:55:20Z","cross_cats_sorted":[],"title_canon_sha256":"2b0de6f087d8313a780c0f19b36690e8fce746093e67591dd6c30bae549449ce","abstract_canon_sha256":"36b32f8332dcb8c39ef1a85f842b175680361a2054d7e07bfa5d9c3bd65d008b"},"schema_version":"1.0"},"canonical_sha256":"ceef1f11846a243133e9d334877309c223779c629b481af07cc0d6f4849012ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:37.040937Z","signature_b64":"oLK+Bu1U9w+UZWdbQcmvdja6+d5R4Sfld21LK2DWom1TiVjz7BvzyG+HA6ukRfNjhts5ckA9xueeV/nNEtDoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ceef1f11846a243133e9d334877309c223779c629b481af07cc0d6f4849012ce","last_reissued_at":"2026-05-18T00:12:37.040187Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:37.040187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.08503","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":"2026-05-18T00:12:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nR//XihxFuxRQkb2EU2VsTQe99Ziad4APiielhYSAnTU6wzotVbbXODIgRhUbh25pYHJHBBjMiRj6v+qB3YNAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:13:38.805061Z"},"content_sha256":"a50659cae46a4be2d3a2c7d8e7a9974e7275591d7895786f6263a9a0c767a44a","schema_version":"1.0","event_id":"sha256:a50659cae46a4be2d3a2c7d8e7a9974e7275591d7895786f6263a9a0c767a44a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:Z3XR6EMENISDCM7J2M2IO4YJYI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Global Semantic Consistency for Zero-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Wu, Jihong Guan, Kai Tian, Shuigeng Zhou","submitted_at":"2018-06-22T05:55:20Z","abstract_excerpt":"In image recognition, there are many cases where training samples cannot cover all target classes. Zero-shot learning (ZSL) utilizes the class semantic information to classify samples of the unseen categories that have no corresponding samples contained in the training set. In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning. We also adopt a soft label embedding loss to further exploit the semantic relations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.08503","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":"2026-05-18T00:12:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i7g5Lch4vUFnBNWhWr621wATZkGUVnA+PACGkTvxFpcOIDeGsWfsRI5DGxiYYEEbZRFH6l29SdfYVLX+CYUqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:13:38.805828Z"},"content_sha256":"17c20199043604471ae00fd85a71421907c4e53fbfda11d7cd52d58c44cab39c","schema_version":"1.0","event_id":"sha256:17c20199043604471ae00fd85a71421907c4e53fbfda11d7cd52d58c44cab39c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/bundle.json","state_url":"https://pith.science/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/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":"2026-05-25T17:13:38Z","links":{"resolver":"https://pith.science/pith/Z3XR6EMENISDCM7J2M2IO4YJYI","bundle":"https://pith.science/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/bundle.json","state":"https://pith.science/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z3XR6EMENISDCM7J2M2IO4YJYI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Z3XR6EMENISDCM7J2M2IO4YJYI","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":"36b32f8332dcb8c39ef1a85f842b175680361a2054d7e07bfa5d9c3bd65d008b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-22T05:55:20Z","title_canon_sha256":"2b0de6f087d8313a780c0f19b36690e8fce746093e67591dd6c30bae549449ce"},"schema_version":"1.0","source":{"id":"1806.08503","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.08503","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"arxiv_version","alias_value":"1806.08503v1","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.08503","created_at":"2026-05-18T00:12:37Z"},{"alias_kind":"pith_short_12","alias_value":"Z3XR6EMENISD","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Z3XR6EMENISDCM7J","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Z3XR6EME","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:17c20199043604471ae00fd85a71421907c4e53fbfda11d7cd52d58c44cab39c","target":"graph","created_at":"2026-05-18T00:12:37Z","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 image recognition, there are many cases where training samples cannot cover all target classes. Zero-shot learning (ZSL) utilizes the class semantic information to classify samples of the unseen categories that have no corresponding samples contained in the training set. In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning. We also adopt a soft label embedding loss to further exploit the semantic relations","authors_text":"Fan Wu, Jihong Guan, Kai Tian, Shuigeng Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-22T05:55:20Z","title":"Global Semantic Consistency for Zero-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.08503","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:a50659cae46a4be2d3a2c7d8e7a9974e7275591d7895786f6263a9a0c767a44a","target":"record","created_at":"2026-05-18T00:12:37Z","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":"36b32f8332dcb8c39ef1a85f842b175680361a2054d7e07bfa5d9c3bd65d008b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-22T05:55:20Z","title_canon_sha256":"2b0de6f087d8313a780c0f19b36690e8fce746093e67591dd6c30bae549449ce"},"schema_version":"1.0","source":{"id":"1806.08503","kind":"arxiv","version":1}},"canonical_sha256":"ceef1f11846a243133e9d334877309c223779c629b481af07cc0d6f4849012ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ceef1f11846a243133e9d334877309c223779c629b481af07cc0d6f4849012ce","first_computed_at":"2026-05-18T00:12:37.040187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:37.040187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oLK+Bu1U9w+UZWdbQcmvdja6+d5R4Sfld21LK2DWom1TiVjz7BvzyG+HA6ukRfNjhts5ckA9xueeV/nNEtDoCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:37.040937Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.08503","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a50659cae46a4be2d3a2c7d8e7a9974e7275591d7895786f6263a9a0c767a44a","sha256:17c20199043604471ae00fd85a71421907c4e53fbfda11d7cd52d58c44cab39c"],"state_sha256":"5c268d399eb43d1c40c2b7bf82b6f73716f6ae21c1ed1eb9e8dea91db98e4c2b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I1A4h03k7fx6mZMov/Gdai9toaGawZpahTFOBoeKX4CdF041qxFnUp6+DqRrwu6+TbsohAn/s9v+1f6TC61DBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:13:38.809972Z","bundle_sha256":"213bb0e6fe0d6715768fd1e0868460f03389fc6a117e4772096c3094f364b0a1"}}