{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:IKPSLKINV63CO3IXRK6W5FRG2A","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":"14f248ee5e6ccde84f709d917a37531b9066c876b3cf0723ed50ebc8358f5534","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T15:49:56Z","title_canon_sha256":"fff8244d3b630f415aa5f0fc1772d33e8aab181924dcd72ae7254f9e2c3d64e6"},"schema_version":"1.0","source":{"id":"2212.10368","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10368","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10368v3","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10368","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_12","alias_value":"IKPSLKINV63C","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"IKPSLKINV63CO3IX","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"IKPSLKIN","created_at":"2026-07-05T07:27:23Z"}],"graph_snapshots":[{"event_id":"sha256:966035276640f95655926ed14cb50da3b9b31914107b68c011e0127fc840ba4a","target":"graph","created_at":"2026-07-05T07:27:23Z","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/2212.10368/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Event cameras asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. However, annotation of event data is a costly and laborious process, which limits the use of deep learning methods for classification and other semantic tasks with the event modality. To reduce the dependency on labeled event data, we introduce Masked Event Modeling (MEM), a self-supervised framework for events. Our method pretrains a neural network on unlabeled events, which can originate from any event camera recording. Subsequently, the pretrained model is finetuned on","authors_text":"Daniel Cremers, David Bonello, Lukas Koestler, Nikita Araslanov, Simon Klenk","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T15:49:56Z","title":"Masked Event Modeling: Self-Supervised Pretraining for Event Cameras"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10368","kind":"arxiv","version":3},"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:fa8d611a64fc859e3a704629ae3f8c62d7fe375a1101ac1f6243b05b36c7f8eb","target":"record","created_at":"2026-07-05T07:27:23Z","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":"14f248ee5e6ccde84f709d917a37531b9066c876b3cf0723ed50ebc8358f5534","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T15:49:56Z","title_canon_sha256":"fff8244d3b630f415aa5f0fc1772d33e8aab181924dcd72ae7254f9e2c3d64e6"},"schema_version":"1.0","source":{"id":"2212.10368","kind":"arxiv","version":3}},"canonical_sha256":"429f25a90dafb6276d178abd6e9626d031862e048c68dbcff0fca523b8e7f52f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"429f25a90dafb6276d178abd6e9626d031862e048c68dbcff0fca523b8e7f52f","first_computed_at":"2026-07-05T07:27:23.427081Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:27:23.427081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jQ3UF3Ks5wMLPP5lNsuMvgbxWw7VhWSBe5WT3PNho9NJFxD5N4z1sFg/3NYYzFVdqIdHZCs8Oa8dC4E+N3O7BA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:27:23.427586Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.10368","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa8d611a64fc859e3a704629ae3f8c62d7fe375a1101ac1f6243b05b36c7f8eb","sha256:966035276640f95655926ed14cb50da3b9b31914107b68c011e0127fc840ba4a"],"state_sha256":"92a1343fb6148a676a914f77c61c0826918ac46913ff68e29eee8177b7490b1c"}