{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZBM5DXO6KK4FEXEXSJHB777TGF","short_pith_number":"pith:ZBM5DXO6","canonical_record":{"source":{"id":"2205.09995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-20T07:25:33Z","cross_cats_sorted":[],"title_canon_sha256":"98752827f44409819d759fd9ef4ce88696597106057f337f7697a6bedb0ef151","abstract_canon_sha256":"23d8c6a621ed3225b7d63eef786bfc97e6fc12bc8695cc1c7839ae1dca5ab603"},"schema_version":"1.0"},"canonical_sha256":"c859d1ddde52b8525c97924e1ffff331791f9d53aa69aeecbd8b8e0b61170ef8","source":{"kind":"arxiv","id":"2205.09995","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09995","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09995v1","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09995","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_12","alias_value":"ZBM5DXO6KK4F","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_16","alias_value":"ZBM5DXO6KK4FEXEX","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_8","alias_value":"ZBM5DXO6","created_at":"2026-07-05T04:25:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZBM5DXO6KK4FEXEXSJHB777TGF","target":"record","payload":{"canonical_record":{"source":{"id":"2205.09995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-20T07:25:33Z","cross_cats_sorted":[],"title_canon_sha256":"98752827f44409819d759fd9ef4ce88696597106057f337f7697a6bedb0ef151","abstract_canon_sha256":"23d8c6a621ed3225b7d63eef786bfc97e6fc12bc8695cc1c7839ae1dca5ab603"},"schema_version":"1.0"},"canonical_sha256":"c859d1ddde52b8525c97924e1ffff331791f9d53aa69aeecbd8b8e0b61170ef8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:02.312673Z","signature_b64":"u9KCpfLBD7YwJV3RoSMqNVke/lROQKx3ANKuP1UhSfYjVWyhqjohaNj1Y4qjfnfGGT8CqlaMmva97EQ/xP1ZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c859d1ddde52b8525c97924e1ffff331791f9d53aa69aeecbd8b8e0b61170ef8","last_reissued_at":"2026-07-05T04:25:02.312215Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:02.312215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.09995","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":"2026-07-05T04:25:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WmIP3Wsiqr+tl1To26Qq66snxzvuiNVJzarHaOHEU+u5o3W0geJ5VwRTT/1hHtzW/E1SsIB8LIvW7Gvd6gG2Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:57:01.888253Z"},"content_sha256":"fe3737555bd5c43d4e2504416bb6c18982d5986d4417a141bf2fdc705fa4098e","schema_version":"1.0","event_id":"sha256:fe3737555bd5c43d4e2504416bb6c18982d5986d4417a141bf2fdc705fa4098e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZBM5DXO6KK4FEXEXSJHB777TGF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhe Li, Changying Li, Dajiang Zhu, David Weizhong Liu, Haixing Dai, Lin Zhao, Lu Zhang, Tianming Liu, Tuo Zhang, Xi Jiang, Yuzhong Chen, Zhenxiang Xiao, Zihao Wu","submitted_at":"2022-05-20T07:25:33Z","abstract_excerpt":"Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances. In this paper, we propose a novel mask-guided vision transformer (MG-ViT) to achieve an effe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09995","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/2205.09995/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":"2026-07-05T04:25:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7yCX2bfphsWR2KZr9frunst0mzhEYzYot4nyN9c2C/ehOPaNlbIkaxvOtDH0m469Uk/8FEeFRR9cOjq0CLZGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:57:01.888651Z"},"content_sha256":"3f25330adb690043dd92536bdf631a936f0b168db915da8b26e94153da765a86","schema_version":"1.0","event_id":"sha256:3f25330adb690043dd92536bdf631a936f0b168db915da8b26e94153da765a86"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/bundle.json","state_url":"https://pith.science/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/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":"2026-07-07T11:57:01Z","links":{"resolver":"https://pith.science/pith/ZBM5DXO6KK4FEXEXSJHB777TGF","bundle":"https://pith.science/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/bundle.json","state":"https://pith.science/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZBM5DXO6KK4FEXEXSJHB777TGF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZBM5DXO6KK4FEXEXSJHB777TGF","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":"23d8c6a621ed3225b7d63eef786bfc97e6fc12bc8695cc1c7839ae1dca5ab603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-20T07:25:33Z","title_canon_sha256":"98752827f44409819d759fd9ef4ce88696597106057f337f7697a6bedb0ef151"},"schema_version":"1.0","source":{"id":"2205.09995","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09995","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09995v1","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09995","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_12","alias_value":"ZBM5DXO6KK4F","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_16","alias_value":"ZBM5DXO6KK4FEXEX","created_at":"2026-07-05T04:25:02Z"},{"alias_kind":"pith_short_8","alias_value":"ZBM5DXO6","created_at":"2026-07-05T04:25:02Z"}],"graph_snapshots":[{"event_id":"sha256:3f25330adb690043dd92536bdf631a936f0b168db915da8b26e94153da765a86","target":"graph","created_at":"2026-07-05T04:25:02Z","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/2205.09995/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances. In this paper, we propose a novel mask-guided vision transformer (MG-ViT) to achieve an effe","authors_text":"Changhe Li, Changying Li, Dajiang Zhu, David Weizhong Liu, Haixing Dai, Lin Zhao, Lu Zhang, Tianming Liu, Tuo Zhang, Xi Jiang, Yuzhong Chen, Zhenxiang Xiao, Zihao Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-20T07:25:33Z","title":"Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09995","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:fe3737555bd5c43d4e2504416bb6c18982d5986d4417a141bf2fdc705fa4098e","target":"record","created_at":"2026-07-05T04:25:02Z","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":"23d8c6a621ed3225b7d63eef786bfc97e6fc12bc8695cc1c7839ae1dca5ab603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-20T07:25:33Z","title_canon_sha256":"98752827f44409819d759fd9ef4ce88696597106057f337f7697a6bedb0ef151"},"schema_version":"1.0","source":{"id":"2205.09995","kind":"arxiv","version":1}},"canonical_sha256":"c859d1ddde52b8525c97924e1ffff331791f9d53aa69aeecbd8b8e0b61170ef8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c859d1ddde52b8525c97924e1ffff331791f9d53aa69aeecbd8b8e0b61170ef8","first_computed_at":"2026-07-05T04:25:02.312215Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:25:02.312215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u9KCpfLBD7YwJV3RoSMqNVke/lROQKx3ANKuP1UhSfYjVWyhqjohaNj1Y4qjfnfGGT8CqlaMmva97EQ/xP1ZAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:25:02.312673Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.09995","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe3737555bd5c43d4e2504416bb6c18982d5986d4417a141bf2fdc705fa4098e","sha256:3f25330adb690043dd92536bdf631a936f0b168db915da8b26e94153da765a86"],"state_sha256":"96e80bc6a1629688d3365fc8d56e5a1d0b00879050236f6035fb61c966e01cd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uwvsNKj5mg+2QBL7yvMaAl1mCQQroUbV0y32wqi0jfeOtv6NRW3A43Kum5vOQWmQSi6cLRgVIvO2DFwfT6QXAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:57:01.890520Z","bundle_sha256":"a16708f6bd012bbfc4aa5f6faf0d4813146012ff7954b86dc1f9cebe81964a03"}}