{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:COOCDZSKTGAKPTSEO3IZWJ6XQ4","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":"4b8aef52573ca79b1a357faa1ba13a8d20eb0d0b080e678681d3d4710ef7b36d","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T17:06:10Z","title_canon_sha256":"ca1ee734df0f2bce300dc47b73127d4fcf1b6bf2b517cb587864821cd5df6f6d"},"schema_version":"1.0","source":{"id":"1806.05147","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05147","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05147v2","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05147","created_at":"2026-05-18T00:13:16Z"},{"alias_kind":"pith_short_12","alias_value":"COOCDZSKTGAK","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"COOCDZSKTGAKPTSE","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"COOCDZSK","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:5abb46fae9dc69592b4676db17e1807db94d554efb8d15d5f0e84a7adc1d1903","target":"graph","created_at":"2026-05-18T00:13:16Z","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":"State-of-the-art deep learning algorithms generally require large amounts of data for model training. Lack thereof can severely deteriorate the performance, particularly in scenarios with fine-grained boundaries between categories. To this end, we propose a multimodal approach that facilitates bridging the information gap by means of meaningful joint embeddings. Specifically, we present a benchmark that is multimodal during training (i.e. images and texts) and single-modal in testing time (i.e. images), with the associated task to utilize multimodal data in base classes (with many samples), to","authors_text":"Frederik Pahde, Moin Nabi, Patrick J\\\"ahnichen, Tassilo Klein","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T17:06:10Z","title":"Cross-modal Hallucination for Few-shot Fine-grained Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05147","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":"sha256:9b5d24aff19212b8f6299ff1523272c327a1d63df4996dcaa0ef70f58d6dcad8","target":"record","created_at":"2026-05-18T00:13:16Z","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":"4b8aef52573ca79b1a357faa1ba13a8d20eb0d0b080e678681d3d4710ef7b36d","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T17:06:10Z","title_canon_sha256":"ca1ee734df0f2bce300dc47b73127d4fcf1b6bf2b517cb587864821cd5df6f6d"},"schema_version":"1.0","source":{"id":"1806.05147","kind":"arxiv","version":2}},"canonical_sha256":"139c21e64a9980a7ce4476d19b27d7870e9a5ae9d04cedb5fe2d1e85c20cd028","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"139c21e64a9980a7ce4476d19b27d7870e9a5ae9d04cedb5fe2d1e85c20cd028","first_computed_at":"2026-05-18T00:13:16.888340Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:16.888340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NvhTGpiifYkMp6fZ8gjJ2ycOZl0XBKNfnZphqs/Ki1He7PSb53P4Jr2dk7O437uHZcXfvmnure7rz1MmNAbwCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:16.888988Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05147","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b5d24aff19212b8f6299ff1523272c327a1d63df4996dcaa0ef70f58d6dcad8","sha256:5abb46fae9dc69592b4676db17e1807db94d554efb8d15d5f0e84a7adc1d1903"],"state_sha256":"951b6dbd78d5f6a2ebbc90e9c8e78f5927b935ce7a1dc4cd06550bb79bfc3eb1"}