{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:TMXABI4XROKRFECGQQL25JFXJN","short_pith_number":"pith:TMXABI4X","canonical_record":{"source":{"id":"2005.04414","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T10:09:13Z","cross_cats_sorted":[],"title_canon_sha256":"0e9f60aab915d34ee18b352e4232e9e60630c0d0416c91b7fb03c7c4a55f45ec","abstract_canon_sha256":"959a7e97691bb33d5c080f4b1ba5069814611fbdbf4e036869b3b80bce7ba896"},"schema_version":"1.0"},"canonical_sha256":"9b2e00a3978b951290468417aea4b74b559b03a33a67e007814758b07b656d8f","source":{"kind":"arxiv","id":"2005.04414","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.04414","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2005.04414v2","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04414","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"TMXABI4XROKR","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"TMXABI4XROKRFECG","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"TMXABI4X","created_at":"2026-07-05T01:02:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:TMXABI4XROKRFECGQQL25JFXJN","target":"record","payload":{"canonical_record":{"source":{"id":"2005.04414","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T10:09:13Z","cross_cats_sorted":[],"title_canon_sha256":"0e9f60aab915d34ee18b352e4232e9e60630c0d0416c91b7fb03c7c4a55f45ec","abstract_canon_sha256":"959a7e97691bb33d5c080f4b1ba5069814611fbdbf4e036869b3b80bce7ba896"},"schema_version":"1.0"},"canonical_sha256":"9b2e00a3978b951290468417aea4b74b559b03a33a67e007814758b07b656d8f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:30.496996Z","signature_b64":"hXUeC88jbfMWeVEuql9XZGdDtjVz70Y8fz6LsaYt3s/zLLJRggRCzA+BDKSuAXt3q8G/2EYNiy32ILKf61mxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b2e00a3978b951290468417aea4b74b559b03a33a67e007814758b07b656d8f","last_reissued_at":"2026-07-05T01:02:30.496593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:30.496593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.04414","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":"2026-07-05T01:02:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/3e2hnNdaKKBxmjv2A73kDbjS1s6TlXhkHay5uZUwaHUsamc5cOxa6biECoQvMz+ZCu1HkjXhe12UWNwMguDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:19:35.068050Z"},"content_sha256":"f0d9a2b82875251e353ac2af3d2023b0ff8f54363b8af06e1040a36e15041bee","schema_version":"1.0","event_id":"sha256:f0d9a2b82875251e353ac2af3d2023b0ff8f54363b8af06e1040a36e15041bee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:TMXABI4XROKRFECGQQL25JFXJN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Memory-Augmented Relation Network for Few-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun He, Meng Wang, Mingliang Xu, Richang Hong, Xueliang Liu, Zhengjun Zha","submitted_at":"2020-05-09T10:09:13Z","abstract_excerpt":"Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances. However, most of them treat each individual instance in the working context separately without considering its relationships with the others. In this work, we investigate a new metric-learning method, Memory-Augmented Relation Network (MRN), to explicitly exploit these relationships. In particular, for an instance, we choose the samples that are visually similar from the worki"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04414","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/2005.04414/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":"2026-07-05T01:02:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NwjFSiVlvUuJqEHOWxB9iyTuNvYJBSwXMfVv2lDDb9NLclqgn/6IrNbxImFK98r0t5mxi2k0/C+kNOuczQ7zBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:19:35.068939Z"},"content_sha256":"b0c5a16789a5deef83f75d0cfcbf719f090d8a5c6814d9f7b6d9349ff87f07cc","schema_version":"1.0","event_id":"sha256:b0c5a16789a5deef83f75d0cfcbf719f090d8a5c6814d9f7b6d9349ff87f07cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TMXABI4XROKRFECGQQL25JFXJN/bundle.json","state_url":"https://pith.science/pith/TMXABI4XROKRFECGQQL25JFXJN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TMXABI4XROKRFECGQQL25JFXJN/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":"2026-08-10T23:19:35Z","links":{"resolver":"https://pith.science/pith/TMXABI4XROKRFECGQQL25JFXJN","bundle":"https://pith.science/pith/TMXABI4XROKRFECGQQL25JFXJN/bundle.json","state":"https://pith.science/pith/TMXABI4XROKRFECGQQL25JFXJN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TMXABI4XROKRFECGQQL25JFXJN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:TMXABI4XROKRFECGQQL25JFXJN","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":"959a7e97691bb33d5c080f4b1ba5069814611fbdbf4e036869b3b80bce7ba896","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T10:09:13Z","title_canon_sha256":"0e9f60aab915d34ee18b352e4232e9e60630c0d0416c91b7fb03c7c4a55f45ec"},"schema_version":"1.0","source":{"id":"2005.04414","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.04414","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2005.04414v2","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04414","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"TMXABI4XROKR","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"TMXABI4XROKRFECG","created_at":"2026-07-05T01:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"TMXABI4X","created_at":"2026-07-05T01:02:30Z"}],"graph_snapshots":[{"event_id":"sha256:b0c5a16789a5deef83f75d0cfcbf719f090d8a5c6814d9f7b6d9349ff87f07cc","target":"graph","created_at":"2026-07-05T01:02:30Z","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/2005.04414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances. However, most of them treat each individual instance in the working context separately without considering its relationships with the others. In this work, we investigate a new metric-learning method, Memory-Augmented Relation Network (MRN), to explicitly exploit these relationships. In particular, for an instance, we choose the samples that are visually similar from the worki","authors_text":"Jun He, Meng Wang, Mingliang Xu, Richang Hong, Xueliang Liu, Zhengjun Zha","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T10:09:13Z","title":"Memory-Augmented Relation Network for Few-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04414","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:f0d9a2b82875251e353ac2af3d2023b0ff8f54363b8af06e1040a36e15041bee","target":"record","created_at":"2026-07-05T01:02:30Z","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":"959a7e97691bb33d5c080f4b1ba5069814611fbdbf4e036869b3b80bce7ba896","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-09T10:09:13Z","title_canon_sha256":"0e9f60aab915d34ee18b352e4232e9e60630c0d0416c91b7fb03c7c4a55f45ec"},"schema_version":"1.0","source":{"id":"2005.04414","kind":"arxiv","version":2}},"canonical_sha256":"9b2e00a3978b951290468417aea4b74b559b03a33a67e007814758b07b656d8f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b2e00a3978b951290468417aea4b74b559b03a33a67e007814758b07b656d8f","first_computed_at":"2026-07-05T01:02:30.496593Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:02:30.496593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hXUeC88jbfMWeVEuql9XZGdDtjVz70Y8fz6LsaYt3s/zLLJRggRCzA+BDKSuAXt3q8G/2EYNiy32ILKf61mxBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:02:30.496996Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.04414","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0d9a2b82875251e353ac2af3d2023b0ff8f54363b8af06e1040a36e15041bee","sha256:b0c5a16789a5deef83f75d0cfcbf719f090d8a5c6814d9f7b6d9349ff87f07cc"],"state_sha256":"e26bff9bae8482687fbf8f48084dcf7436dc63f8eed3234b689a6a02c27d2d8f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NA689jvP9+27aZTsBnJO4A4hLOxnobu/H2mXx6zRLDxqvL+A5fejE9eNxsckCKlT+itYm8miAqemxE0ChNDvCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T23:19:35.075180Z","bundle_sha256":"cb3fb7248803e4de3c0f457d559a0b9c8f8783e9de6f0c8f5af6884e1e8ee178"}}