{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7OVOCJJL3MGGSYGNB6D36CYTYT","short_pith_number":"pith:7OVOCJJL","canonical_record":{"source":{"id":"2103.13716","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-25T09:47:18Z","cross_cats_sorted":[],"title_canon_sha256":"3d9b0c9fea4d8488d80746fa88c99eea6faa7f293a45c2bf940db79917b47776","abstract_canon_sha256":"f9edb571f9a7529e31579c45d0b5adfafd6e6510f88b0a5441780dbb3b2db936"},"schema_version":"1.0"},"canonical_sha256":"fbaae1252bdb0c6960cd0f87bf0b13c4c374922d9b7afbe12eee24846fd9eb9f","source":{"kind":"arxiv","id":"2103.13716","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.13716","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2103.13716v1","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.13716","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"7OVOCJJL3MGG","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"7OVOCJJL3MGGSYGN","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"7OVOCJJL","created_at":"2026-07-05T02:26:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7OVOCJJL3MGGSYGNB6D36CYTYT","target":"record","payload":{"canonical_record":{"source":{"id":"2103.13716","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-25T09:47:18Z","cross_cats_sorted":[],"title_canon_sha256":"3d9b0c9fea4d8488d80746fa88c99eea6faa7f293a45c2bf940db79917b47776","abstract_canon_sha256":"f9edb571f9a7529e31579c45d0b5adfafd6e6510f88b0a5441780dbb3b2db936"},"schema_version":"1.0"},"canonical_sha256":"fbaae1252bdb0c6960cd0f87bf0b13c4c374922d9b7afbe12eee24846fd9eb9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:21.848824Z","signature_b64":"H297iArBKREELkcddjXtuxyhIVq/nTeHv1Ahwmc6Do3TcKrKtydgiOhQtIAqezqRHGgS6D5DrGzvAKo3UYkbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbaae1252bdb0c6960cd0f87bf0b13c4c374922d9b7afbe12eee24846fd9eb9f","last_reissued_at":"2026-07-05T02:26:21.848493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:21.848493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.13716","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":"2026-07-05T02:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"od8gGglqahyIHeNbNrxUqyL4dJ5M1c3fMEnwxCaQ2DA7F2MWJ+FueUyofJEt/qPnsI2BSADfGoXGyTxwNNMxBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:45:57.664590Z"},"content_sha256":"aff4553b930bc9bb8de28867e75ad014ca93ca3595424ec893dc5b3938555930","schema_version":"1.0","event_id":"sha256:aff4553b930bc9bb8de28867e75ad014ca93ca3595424ec893dc5b3938555930"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7OVOCJJL3MGGSYGNB6D36CYTYT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Tao Xiang, Timothy M. Hospedales, Yi-Zhe Song, Yongxin Yang","submitted_at":"2021-03-25T09:47:18Z","abstract_excerpt":"Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. However supervision-free pre-text tasks are challenging to design and usually modality specific. Although there is a rich literature of self-supervised methods for either spatial (such as images) or temporal data (sound or text) modalities, a common pre-text task that benefits both modalities is largely missing. In this paper, we are interested in defining a self-supervised pre-text task for sketche"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.13716","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/2103.13716/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":"2026-07-05T02:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wm1vQIaBH/10UyedxvscHd62yNo/yd4C9Xv2SDLCA5mSBG9qkqMfI5kPUHTIU/+fjHbmBlV+J5gSkOaJud4VAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:45:57.665210Z"},"content_sha256":"d6c78c43a408b80cfcc841dec2bfe8bf7de5ef8d1f8eafd269ab0fc96a56103d","schema_version":"1.0","event_id":"sha256:d6c78c43a408b80cfcc841dec2bfe8bf7de5ef8d1f8eafd269ab0fc96a56103d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/bundle.json","state_url":"https://pith.science/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/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":"2026-08-10T18:45:57Z","links":{"resolver":"https://pith.science/pith/7OVOCJJL3MGGSYGNB6D36CYTYT","bundle":"https://pith.science/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/bundle.json","state":"https://pith.science/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7OVOCJJL3MGGSYGNB6D36CYTYT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7OVOCJJL3MGGSYGNB6D36CYTYT","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":"f9edb571f9a7529e31579c45d0b5adfafd6e6510f88b0a5441780dbb3b2db936","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-25T09:47:18Z","title_canon_sha256":"3d9b0c9fea4d8488d80746fa88c99eea6faa7f293a45c2bf940db79917b47776"},"schema_version":"1.0","source":{"id":"2103.13716","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.13716","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2103.13716v1","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.13716","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"7OVOCJJL3MGG","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"7OVOCJJL3MGGSYGN","created_at":"2026-07-05T02:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"7OVOCJJL","created_at":"2026-07-05T02:26:21Z"}],"graph_snapshots":[{"event_id":"sha256:d6c78c43a408b80cfcc841dec2bfe8bf7de5ef8d1f8eafd269ab0fc96a56103d","target":"graph","created_at":"2026-07-05T02:26:21Z","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/2103.13716/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. However supervision-free pre-text tasks are challenging to design and usually modality specific. Although there is a rich literature of self-supervised methods for either spatial (such as images) or temporal data (sound or text) modalities, a common pre-text task that benefits both modalities is largely missing. In this paper, we are interested in defining a self-supervised pre-text task for sketche","authors_text":"Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Tao Xiang, Timothy M. Hospedales, Yi-Zhe Song, Yongxin Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-25T09:47:18Z","title":"Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.13716","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:aff4553b930bc9bb8de28867e75ad014ca93ca3595424ec893dc5b3938555930","target":"record","created_at":"2026-07-05T02:26:21Z","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":"f9edb571f9a7529e31579c45d0b5adfafd6e6510f88b0a5441780dbb3b2db936","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-25T09:47:18Z","title_canon_sha256":"3d9b0c9fea4d8488d80746fa88c99eea6faa7f293a45c2bf940db79917b47776"},"schema_version":"1.0","source":{"id":"2103.13716","kind":"arxiv","version":1}},"canonical_sha256":"fbaae1252bdb0c6960cd0f87bf0b13c4c374922d9b7afbe12eee24846fd9eb9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbaae1252bdb0c6960cd0f87bf0b13c4c374922d9b7afbe12eee24846fd9eb9f","first_computed_at":"2026-07-05T02:26:21.848493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:26:21.848493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"H297iArBKREELkcddjXtuxyhIVq/nTeHv1Ahwmc6Do3TcKrKtydgiOhQtIAqezqRHGgS6D5DrGzvAKo3UYkbAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:26:21.848824Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.13716","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aff4553b930bc9bb8de28867e75ad014ca93ca3595424ec893dc5b3938555930","sha256:d6c78c43a408b80cfcc841dec2bfe8bf7de5ef8d1f8eafd269ab0fc96a56103d"],"state_sha256":"6dac93216797cd38ff1e2714333cbf9aae57b341c30e2260b05b2f14f220d778"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dSM8nmIkINOTxuuWDElVDOhsu/KLRLjM8XFLKXcoJO1A835omCkLbMO3sL6qk9IchHSbXrj7CvWOVNGPlr/bCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T18:45:57.671675Z","bundle_sha256":"f9f32ab22c687c37829b9564bab3b595b84db50f5dedfd192a1f391b3ced461b"}}