{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:ZQGHW573CCMRAVZNDT5R4JTRZM","short_pith_number":"pith:ZQGHW573","canonical_record":{"source":{"id":"2005.04621","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-10T10:11:16Z","cross_cats_sorted":[],"title_canon_sha256":"f78d2b8887083757f14f288365c91db5a0f390c50ed7cc9eba8f76006085d0b7","abstract_canon_sha256":"8a718a3ec15283bb270bd390e37ca321e33b92ad12e6a91c5f5c2396009c966c"},"schema_version":"1.0"},"canonical_sha256":"cc0c7b77fb109910572d1cfb1e2671cb3a79afac7bdd81584694414f623bf6e5","source":{"kind":"arxiv","id":"2005.04621","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.04621","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"arxiv_version","alias_value":"2005.04621v2","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04621","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZQGHW573CCMR","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZQGHW573CCMRAVZN","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZQGHW573","created_at":"2026-07-05T01:46:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:ZQGHW573CCMRAVZNDT5R4JTRZM","target":"record","payload":{"canonical_record":{"source":{"id":"2005.04621","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-10T10:11:16Z","cross_cats_sorted":[],"title_canon_sha256":"f78d2b8887083757f14f288365c91db5a0f390c50ed7cc9eba8f76006085d0b7","abstract_canon_sha256":"8a718a3ec15283bb270bd390e37ca321e33b92ad12e6a91c5f5c2396009c966c"},"schema_version":"1.0"},"canonical_sha256":"cc0c7b77fb109910572d1cfb1e2671cb3a79afac7bdd81584694414f623bf6e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:46:01.398147Z","signature_b64":"p5c+tPfE/vpOjslRXyV9HADga7dP16TFAmtadZsfKKMJfr78/97rnobw3DSHlTfhTkia/3mFa7VlGL7fZe9/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc0c7b77fb109910572d1cfb1e2671cb3a79afac7bdd81584694414f623bf6e5","last_reissued_at":"2026-07-05T01:46:01.397675Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:46:01.397675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.04621","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":"2026-07-05T01:46:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uho2EzN+YXodmjpDKm/S4ng2HNLkyL6y5T1zoVLZxv4n2NkqHWd0KJ47QAQmvRwyJghrgtE+bmwz0Qs4QU/RDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:19:33.338866Z"},"content_sha256":"54e6a995c1cec60831d129a53ef24f533971763c8bb0f6d025f9a3ebbdce2e26","schema_version":"1.0","event_id":"sha256:54e6a995c1cec60831d129a53ef24f533971763c8bb0f6d025f9a3ebbdce2e26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:ZQGHW573CCMRAVZNDT5R4JTRZM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jose Vazquez, Mateusz Ochal, Sen Wang, Yvan Petillot","submitted_at":"2020-05-10T10:11:16Z","abstract_excerpt":"Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images. Many such methods require hundreds, if not thousands, of images per class to generalize well to unseen examples. However, obtaining and labeling sufficiently large volumes of data can be relatively costly and time-consuming, especially when observing rare objects or performing real-time operations. Few-Shot Learning (FSL) efforts have produced many promising methods to deal with low data availability. However, little attention has been given in the underwater domai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04621","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/2005.04621/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":"2026-07-05T01:46:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5xahwZaAqrEhf9VO5TVShO5Pj1q46YXuE1OVlyKWoVn1rM4MI5OM+tSLV1BmEy29nDlrKmt6KRU1OuNL8B5cCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:19:33.339380Z"},"content_sha256":"343b93458bda5db40427eaebaf98cc3edfa42f38a79c13378a6fdde226d66f7c","schema_version":"1.0","event_id":"sha256:343b93458bda5db40427eaebaf98cc3edfa42f38a79c13378a6fdde226d66f7c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/bundle.json","state_url":"https://pith.science/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/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":"2026-08-03T16:19:33Z","links":{"resolver":"https://pith.science/pith/ZQGHW573CCMRAVZNDT5R4JTRZM","bundle":"https://pith.science/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/bundle.json","state":"https://pith.science/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZQGHW573CCMRAVZNDT5R4JTRZM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:ZQGHW573CCMRAVZNDT5R4JTRZM","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":"8a718a3ec15283bb270bd390e37ca321e33b92ad12e6a91c5f5c2396009c966c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-10T10:11:16Z","title_canon_sha256":"f78d2b8887083757f14f288365c91db5a0f390c50ed7cc9eba8f76006085d0b7"},"schema_version":"1.0","source":{"id":"2005.04621","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.04621","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"arxiv_version","alias_value":"2005.04621v2","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04621","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZQGHW573CCMR","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZQGHW573CCMRAVZN","created_at":"2026-07-05T01:46:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZQGHW573","created_at":"2026-07-05T01:46:01Z"}],"graph_snapshots":[{"event_id":"sha256:343b93458bda5db40427eaebaf98cc3edfa42f38a79c13378a6fdde226d66f7c","target":"graph","created_at":"2026-07-05T01:46:01Z","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/2005.04621/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images. Many such methods require hundreds, if not thousands, of images per class to generalize well to unseen examples. However, obtaining and labeling sufficiently large volumes of data can be relatively costly and time-consuming, especially when observing rare objects or performing real-time operations. Few-Shot Learning (FSL) efforts have produced many promising methods to deal with low data availability. However, little attention has been given in the underwater domai","authors_text":"Jose Vazquez, Mateusz Ochal, Sen Wang, Yvan Petillot","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-10T10:11:16Z","title":"A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04621","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":"sha256:54e6a995c1cec60831d129a53ef24f533971763c8bb0f6d025f9a3ebbdce2e26","target":"record","created_at":"2026-07-05T01:46:01Z","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":"8a718a3ec15283bb270bd390e37ca321e33b92ad12e6a91c5f5c2396009c966c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-10T10:11:16Z","title_canon_sha256":"f78d2b8887083757f14f288365c91db5a0f390c50ed7cc9eba8f76006085d0b7"},"schema_version":"1.0","source":{"id":"2005.04621","kind":"arxiv","version":2}},"canonical_sha256":"cc0c7b77fb109910572d1cfb1e2671cb3a79afac7bdd81584694414f623bf6e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc0c7b77fb109910572d1cfb1e2671cb3a79afac7bdd81584694414f623bf6e5","first_computed_at":"2026-07-05T01:46:01.397675Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:46:01.397675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p5c+tPfE/vpOjslRXyV9HADga7dP16TFAmtadZsfKKMJfr78/97rnobw3DSHlTfhTkia/3mFa7VlGL7fZe9/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:46:01.398147Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.04621","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54e6a995c1cec60831d129a53ef24f533971763c8bb0f6d025f9a3ebbdce2e26","sha256:343b93458bda5db40427eaebaf98cc3edfa42f38a79c13378a6fdde226d66f7c"],"state_sha256":"a62cd4f9ec38db0ee0ecd33104cc8ea0d835f17945665ba15a6cf870e0de65f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eNbVHUKUm9EGwjdv13iM0baq7EOxiYPQp8+eYbq47EQ1IjB5Rl31H1COXsW8k0F86pKXBHd8jaE1kRn8EZ3eDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:19:33.344575Z","bundle_sha256":"a50cd35282f098a9bcc89a851ef7f2480150321e71f25599ed2f9090feffab03"}}