{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HJIODHYEUGG6CL5AFWSV3DV4VD","short_pith_number":"pith:HJIODHYE","canonical_record":{"source":{"id":"1901.01868","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-04T12:19:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"426a074898965d944e22e216eddccb1fc3b484565d3f9be2956aa13955e325b7","abstract_canon_sha256":"696c75bb2dba8a0eaef455b325dce0bad9abebc00af48210de804e1baf17dd68"},"schema_version":"1.0"},"canonical_sha256":"3a50e19f04a18de12fa02da55d8ebca8f6b75ffbe64f65436c62361e3e21f041","source":{"kind":"arxiv","id":"1901.01868","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01868","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01868v1","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01868","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"pith_short_12","alias_value":"HJIODHYEUGG6","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HJIODHYEUGG6CL5A","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HJIODHYE","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HJIODHYEUGG6CL5AFWSV3DV4VD","target":"record","payload":{"canonical_record":{"source":{"id":"1901.01868","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-04T12:19:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"426a074898965d944e22e216eddccb1fc3b484565d3f9be2956aa13955e325b7","abstract_canon_sha256":"696c75bb2dba8a0eaef455b325dce0bad9abebc00af48210de804e1baf17dd68"},"schema_version":"1.0"},"canonical_sha256":"3a50e19f04a18de12fa02da55d8ebca8f6b75ffbe64f65436c62361e3e21f041","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:49.447536Z","signature_b64":"kaFZdnxG+4uDdUYB3R9Tx0UUazgZjUSCFzrEnsCJYDT9eQuTfiNzhVB2x9gYST3dF/0U+wYY0TOE8SZamp2RCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a50e19f04a18de12fa02da55d8ebca8f6b75ffbe64f65436c62361e3e21f041","last_reissued_at":"2026-05-17T23:56:49.447072Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:49.447072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.01868","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":"2026-05-17T23:56:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CCyZoivseqGWAq3V06l4SKRIV+MDwPXuFyI6rmTP4j5LV+VjX2aFEG02ITuPK88mh7fVM3GfSd5Bsr/ywne8AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:58:15.283358Z"},"content_sha256":"bd86ff1707ce32d66e743d2749d5ebbb446917274eae67dc6b5e8741f2574191","schema_version":"1.0","event_id":"sha256:bd86ff1707ce32d66e743d2749d5ebbb446917274eae67dc6b5e8741f2574191"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HJIODHYEUGG6CL5AFWSV3DV4VD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-Shot Learning from Imaginary 3D Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Frederik Pahde, Jannik Wolff, Mihai Puscas, Moin Nabi, Nicu Sebe, Tassilo Klein","submitted_at":"2019-01-04T12:19:58Z","abstract_excerpt":"Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01868","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":"2026-05-17T23:56:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/uKxjTizpA2y+aGF6ZFuMo93AVHYgX/FYKyQwq9NqYXCqz5WxCJegTCoGgTnTUy/cPuyC3x4Bir5GDv/Z0NdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:58:15.283741Z"},"content_sha256":"298fe2f6dc493a6f99da5921f1c9cc3bb08f9e80508d24a9912868b03fa8af0a","schema_version":"1.0","event_id":"sha256:298fe2f6dc493a6f99da5921f1c9cc3bb08f9e80508d24a9912868b03fa8af0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/bundle.json","state_url":"https://pith.science/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/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":"2026-05-26T12:58:15Z","links":{"resolver":"https://pith.science/pith/HJIODHYEUGG6CL5AFWSV3DV4VD","bundle":"https://pith.science/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/bundle.json","state":"https://pith.science/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HJIODHYEUGG6CL5AFWSV3DV4VD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HJIODHYEUGG6CL5AFWSV3DV4VD","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":"696c75bb2dba8a0eaef455b325dce0bad9abebc00af48210de804e1baf17dd68","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-04T12:19:58Z","title_canon_sha256":"426a074898965d944e22e216eddccb1fc3b484565d3f9be2956aa13955e325b7"},"schema_version":"1.0","source":{"id":"1901.01868","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01868","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01868v1","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01868","created_at":"2026-05-17T23:56:49Z"},{"alias_kind":"pith_short_12","alias_value":"HJIODHYEUGG6","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HJIODHYEUGG6CL5A","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HJIODHYE","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:298fe2f6dc493a6f99da5921f1c9cc3bb08f9e80508d24a9912868b03fa8af0a","target":"graph","created_at":"2026-05-17T23:56:49Z","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":"Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the ","authors_text":"Frederik Pahde, Jannik Wolff, Mihai Puscas, Moin Nabi, Nicu Sebe, Tassilo Klein","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-04T12:19:58Z","title":"Low-Shot Learning from Imaginary 3D Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01868","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:bd86ff1707ce32d66e743d2749d5ebbb446917274eae67dc6b5e8741f2574191","target":"record","created_at":"2026-05-17T23:56:49Z","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":"696c75bb2dba8a0eaef455b325dce0bad9abebc00af48210de804e1baf17dd68","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-04T12:19:58Z","title_canon_sha256":"426a074898965d944e22e216eddccb1fc3b484565d3f9be2956aa13955e325b7"},"schema_version":"1.0","source":{"id":"1901.01868","kind":"arxiv","version":1}},"canonical_sha256":"3a50e19f04a18de12fa02da55d8ebca8f6b75ffbe64f65436c62361e3e21f041","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a50e19f04a18de12fa02da55d8ebca8f6b75ffbe64f65436c62361e3e21f041","first_computed_at":"2026-05-17T23:56:49.447072Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:49.447072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kaFZdnxG+4uDdUYB3R9Tx0UUazgZjUSCFzrEnsCJYDT9eQuTfiNzhVB2x9gYST3dF/0U+wYY0TOE8SZamp2RCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:49.447536Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.01868","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd86ff1707ce32d66e743d2749d5ebbb446917274eae67dc6b5e8741f2574191","sha256:298fe2f6dc493a6f99da5921f1c9cc3bb08f9e80508d24a9912868b03fa8af0a"],"state_sha256":"6d8f275eba4616c7e9a76237857685d0347dc9221aa64a550cc86fa6dbc4561a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EXfr8t0YMsPUd/yGRqN8ggiq8slBzdKMa3wpDULJJ5NwVbMuAV53nuBe+59vdTOhbQ9o/B99FC7s81Ks9+rTCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T12:58:15.286464Z","bundle_sha256":"d09f319e926b45e5136279f29f73e80bd8eadb8b2fd519bd647e120b36c12aaa"}}