{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:S73SKB5RGZIRTC4EOBHYJIVIWP","short_pith_number":"pith:S73SKB5R","schema_version":"1.0","canonical_sha256":"97f72507b13651198b84704f84a2a8b3f01e15bc8497cffa918d59831aff0eae","source":{"kind":"arxiv","id":"2504.16023","version":2},"attestation_state":"computed","paper":{"title":"PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunyong Hu, Gongfan Fang, Jianke Zhu, Jianyun Xu, Lingdong Kong, Song Wang, Wentong Li, Xiaolu Liu, Xinchao Wang","submitted_at":"2025-04-22T16:41:21Z","abstract_excerpt":"Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. Parameter-efficient fine-tuning (PEFT) methods offer a promising solution to mitigate these resource requirements, yet most current approaches rely on complex adapter and prompt mechanisms that increase tunable parameters. In this paper, we propose PointLoRA, a simple yet effective method"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.16023","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-22T16:41:21Z","cross_cats_sorted":[],"title_canon_sha256":"0138345693ea61ee593f488c8eb39120ad79b3df58b4b2160917eca8f5526a58","abstract_canon_sha256":"5131640c33d8395e7b7ad8c18a15a1e3592576a1867fece379e9323814404a54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:37.585707Z","signature_b64":"SEWhPkB0/Z0ltqkpWrNh6j8bzNV4Gn+ccaVuNM/CyUf8Q8tT3SlQRQTRtTAKbiIN2d2se0g/GMOgAe8EK7mQCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97f72507b13651198b84704f84a2a8b3f01e15bc8497cffa918d59831aff0eae","last_reissued_at":"2026-07-05T11:10:37.585174Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:37.585174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunyong Hu, Gongfan Fang, Jianke Zhu, Jianyun Xu, Lingdong Kong, Song Wang, Wentong Li, Xiaolu Liu, Xinchao Wang","submitted_at":"2025-04-22T16:41:21Z","abstract_excerpt":"Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. Parameter-efficient fine-tuning (PEFT) methods offer a promising solution to mitigate these resource requirements, yet most current approaches rely on complex adapter and prompt mechanisms that increase tunable parameters. In this paper, we propose PointLoRA, a simple yet effective method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16023","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/2504.16023/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.16023","created_at":"2026-07-05T11:10:37.585241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.16023v2","created_at":"2026-07-05T11:10:37.585241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16023","created_at":"2026-07-05T11:10:37.585241+00:00"},{"alias_kind":"pith_short_12","alias_value":"S73SKB5RGZIR","created_at":"2026-07-05T11:10:37.585241+00:00"},{"alias_kind":"pith_short_16","alias_value":"S73SKB5RGZIRTC4E","created_at":"2026-07-05T11:10:37.585241+00:00"},{"alias_kind":"pith_short_8","alias_value":"S73SKB5R","created_at":"2026-07-05T11:10:37.585241+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP","json":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP.json","graph_json":"https://pith.science/api/pith-number/S73SKB5RGZIRTC4EOBHYJIVIWP/graph.json","events_json":"https://pith.science/api/pith-number/S73SKB5RGZIRTC4EOBHYJIVIWP/events.json","paper":"https://pith.science/paper/S73SKB5R"},"agent_actions":{"view_html":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP","download_json":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP.json","view_paper":"https://pith.science/paper/S73SKB5R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.16023&json=true","fetch_graph":"https://pith.science/api/pith-number/S73SKB5RGZIRTC4EOBHYJIVIWP/graph.json","fetch_events":"https://pith.science/api/pith-number/S73SKB5RGZIRTC4EOBHYJIVIWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP/action/storage_attestation","attest_author":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP/action/author_attestation","sign_citation":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP/action/citation_signature","submit_replication":"https://pith.science/pith/S73SKB5RGZIRTC4EOBHYJIVIWP/action/replication_record"}},"created_at":"2026-07-05T11:10:37.585241+00:00","updated_at":"2026-07-05T11:10:37.585241+00:00"}