{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UIVKBHCHFNWOWWVWBQENYQHOYE","short_pith_number":"pith:UIVKBHCH","canonical_record":{"source":{"id":"2008.00247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-08-01T11:23:37Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"7ad381cf4eea8f785ffa254fa4e87c84435f2d8b0f8247cd01ec01d34733a18a","abstract_canon_sha256":"b45659228f11eb8c0112615de78afa2d6f22bd9558658396087d0d5b809a259e"},"schema_version":"1.0"},"canonical_sha256":"a22aa09c472b6ceb5ab60c08dc40eec13c2356aae19ef410676e3148c3f729db","source":{"kind":"arxiv","id":"2008.00247","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.00247","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"arxiv_version","alias_value":"2008.00247v1","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.00247","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_12","alias_value":"UIVKBHCHFNWO","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_16","alias_value":"UIVKBHCHFNWOWWVW","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_8","alias_value":"UIVKBHCH","created_at":"2026-07-05T01:24:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UIVKBHCHFNWOWWVWBQENYQHOYE","target":"record","payload":{"canonical_record":{"source":{"id":"2008.00247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-08-01T11:23:37Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"7ad381cf4eea8f785ffa254fa4e87c84435f2d8b0f8247cd01ec01d34733a18a","abstract_canon_sha256":"b45659228f11eb8c0112615de78afa2d6f22bd9558658396087d0d5b809a259e"},"schema_version":"1.0"},"canonical_sha256":"a22aa09c472b6ceb5ab60c08dc40eec13c2356aae19ef410676e3148c3f729db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:24:05.924783Z","signature_b64":"UEx11jGDKm75msrT2KT5MZDF0lZ3HrFo3q1i0bjeaTfzdb4uo+6+vPWwulKje+OfiYudxplCHSrSQ9CUI1uzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a22aa09c472b6ceb5ab60c08dc40eec13c2356aae19ef410676e3148c3f729db","last_reissued_at":"2026-07-05T01:24:05.924258Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:24:05.924258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.00247","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":"2026-07-05T01:24:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"07N8X4DUmyCE/HzebtCrXrsctP87Izld5xs07o3hTfmGSN19oHL1ADdarA2ym2o+FZbTA4m9gP+wQMToXunTBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:37:05.097778Z"},"content_sha256":"597024a9009e11a60baf20f1bed0f920a4efcfe6d64221bd99ae99977cdce5fa","schema_version":"1.0","event_id":"sha256:597024a9009e11a60baf20f1bed0f920a4efcfe6d64221bd99ae99977cdce5fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UIVKBHCHFNWOWWVWBQENYQHOYE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-DRN: Meta-Learning for 1-Shot Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Atmadeep Banerjee","submitted_at":"2020-08-01T11:23:37Z","abstract_excerpt":"Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.00247","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/2008.00247/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":"2026-07-05T01:24:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G7y6ddV0uwrtSr0DqpyFfZQOFDLW/TF+yQ/nOH7GSxLumHTnzwg6l99+HgLI1n7CVaZ5pPOBVgT4bEfD77m8AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:37:05.098170Z"},"content_sha256":"194b320b6c361bc90d2ebcab8fc8108607c0a68d72a57b2113b0d2f12cd229ac","schema_version":"1.0","event_id":"sha256:194b320b6c361bc90d2ebcab8fc8108607c0a68d72a57b2113b0d2f12cd229ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/bundle.json","state_url":"https://pith.science/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/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":"2026-08-03T15:37:05Z","links":{"resolver":"https://pith.science/pith/UIVKBHCHFNWOWWVWBQENYQHOYE","bundle":"https://pith.science/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/bundle.json","state":"https://pith.science/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UIVKBHCHFNWOWWVWBQENYQHOYE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UIVKBHCHFNWOWWVWBQENYQHOYE","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":"b45659228f11eb8c0112615de78afa2d6f22bd9558658396087d0d5b809a259e","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-08-01T11:23:37Z","title_canon_sha256":"7ad381cf4eea8f785ffa254fa4e87c84435f2d8b0f8247cd01ec01d34733a18a"},"schema_version":"1.0","source":{"id":"2008.00247","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.00247","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"arxiv_version","alias_value":"2008.00247v1","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.00247","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_12","alias_value":"UIVKBHCHFNWO","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_16","alias_value":"UIVKBHCHFNWOWWVW","created_at":"2026-07-05T01:24:05Z"},{"alias_kind":"pith_short_8","alias_value":"UIVKBHCH","created_at":"2026-07-05T01:24:05Z"}],"graph_snapshots":[{"event_id":"sha256:194b320b6c361bc90d2ebcab8fc8108607c0a68d72a57b2113b0d2f12cd229ac","target":"graph","created_at":"2026-07-05T01:24:05Z","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/2008.00247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked","authors_text":"Atmadeep Banerjee","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-08-01T11:23:37Z","title":"Meta-DRN: Meta-Learning for 1-Shot Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.00247","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":"sha256:597024a9009e11a60baf20f1bed0f920a4efcfe6d64221bd99ae99977cdce5fa","target":"record","created_at":"2026-07-05T01:24:05Z","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":"b45659228f11eb8c0112615de78afa2d6f22bd9558658396087d0d5b809a259e","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-08-01T11:23:37Z","title_canon_sha256":"7ad381cf4eea8f785ffa254fa4e87c84435f2d8b0f8247cd01ec01d34733a18a"},"schema_version":"1.0","source":{"id":"2008.00247","kind":"arxiv","version":1}},"canonical_sha256":"a22aa09c472b6ceb5ab60c08dc40eec13c2356aae19ef410676e3148c3f729db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a22aa09c472b6ceb5ab60c08dc40eec13c2356aae19ef410676e3148c3f729db","first_computed_at":"2026-07-05T01:24:05.924258Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:24:05.924258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UEx11jGDKm75msrT2KT5MZDF0lZ3HrFo3q1i0bjeaTfzdb4uo+6+vPWwulKje+OfiYudxplCHSrSQ9CUI1uzBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:24:05.924783Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.00247","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:597024a9009e11a60baf20f1bed0f920a4efcfe6d64221bd99ae99977cdce5fa","sha256:194b320b6c361bc90d2ebcab8fc8108607c0a68d72a57b2113b0d2f12cd229ac"],"state_sha256":"19694a59b42cd2fcdfbc49f5d197da3b5814bf683169fc22ebed724f475b3d76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ekjYy/fUnwGlp05fUG5i2HR0cyDEFTMd2zjfvT8QwIeZsb1m/e+OoDBSy4TARyNbXO0ordYkcAZ3mRr/hnsKBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T15:37:05.101483Z","bundle_sha256":"98740ebe6fddd8e0901efaced4fb0327731f5f1fb89e71d1f2f2792dbd1b3fcc"}}