{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:57QW52ZAZTC5UKPJV6LMVOXJLK","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":"9481841f7c061c688b9cec9605b21fb130cc689ed5e44c5469f216928f4e89b9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-14T03:22:03Z","title_canon_sha256":"f6c1137274f5e5d18a9e8b4a7692d0b6d5cf688ee5372e2ff4dfe28c142a0913"},"schema_version":"1.0","source":{"id":"2211.07084","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07084","created_at":"2026-07-05T05:16:04Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07084v2","created_at":"2026-07-05T05:16:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07084","created_at":"2026-07-05T05:16:04Z"},{"alias_kind":"pith_short_12","alias_value":"57QW52ZAZTC5","created_at":"2026-07-05T05:16:04Z"},{"alias_kind":"pith_short_16","alias_value":"57QW52ZAZTC5UKPJ","created_at":"2026-07-05T05:16:04Z"},{"alias_kind":"pith_short_8","alias_value":"57QW52ZA","created_at":"2026-07-05T05:16:04Z"}],"graph_snapshots":[{"event_id":"sha256:10a4764a764398069f08b2971c730e08688013f55fe8055e6f20005b6b9ba3e7","target":"graph","created_at":"2026-07-05T05:16:04Z","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/2211.07084/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D object detection methods directly follow the practice of fully-supervised methods to augment labeled and unlabeled data, which is sub-optimal. In this paper, we design a data augmentation method for semi-supervised learning, which we call Semi-Sampling. Specifically, we use ground truth labels and pseudo labels to crop gt samples and pseudo samples on labeled frames and unlabeled frames, respectively. Then we can genera","authors_text":"Binbin Lin, Deng Cai, Haifeng Liu, Hua Chen, Liang Peng, Wanli Ouyang, Xiaopei Wu, Xiaoshui Huang, Yang Zhao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-14T03:22:03Z","title":"Boosting Semi-Supervised 3D Object Detection with Semi-Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07084","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:fbc971ad754f1c1d71e8f266d2daa36b4f88b01327ea5663ceb6478136dff4ed","target":"record","created_at":"2026-07-05T05:16:04Z","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":"9481841f7c061c688b9cec9605b21fb130cc689ed5e44c5469f216928f4e89b9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-14T03:22:03Z","title_canon_sha256":"f6c1137274f5e5d18a9e8b4a7692d0b6d5cf688ee5372e2ff4dfe28c142a0913"},"schema_version":"1.0","source":{"id":"2211.07084","kind":"arxiv","version":2}},"canonical_sha256":"efe16eeb20ccc5da29e9af96cabae95ab7d956140f381f26420227ff4afab4f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efe16eeb20ccc5da29e9af96cabae95ab7d956140f381f26420227ff4afab4f3","first_computed_at":"2026-07-05T05:16:04.138733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:04.138733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5lQY7Mvp1J2dXCWOAzrYI6zIbv8M62B3OOnEmi/dxHC7Nbp+l+ljD/4VV2lSzbp4fDPRoyEAt4/XObNYYRvbAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:04.139279Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.07084","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbc971ad754f1c1d71e8f266d2daa36b4f88b01327ea5663ceb6478136dff4ed","sha256:10a4764a764398069f08b2971c730e08688013f55fe8055e6f20005b6b9ba3e7"],"state_sha256":"f3d2f99875dbf519dee84f4cb876c30924eebadeb5415fa26a6128c0f840b4cd"}