{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G3BQO35CCIBV5FGNJW3ETDTYUP","short_pith_number":"pith:G3BQO35C","schema_version":"1.0","canonical_sha256":"36c3076fa212035e94cd4db6498e78a3fb4f54c8a9cf5202c24ab1f0e3788c17","source":{"kind":"arxiv","id":"2406.17413","version":1},"attestation_state":"computed","paper":{"title":"Depth-Guided Semi-Supervised Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianghang Lin, Jie Hu, Liujuan Cao, Rongrong Ji, Xiawu Zheng, Xin Chen","submitted_at":"2024-06-25T09:36:50Z","abstract_excerpt":"Semi-Supervised Instance Segmentation (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled images to generate pseudo-labels. However, such a mechanism often introduces unstable noise, as a single instance can display multiple RGB values. To overcome this limitation, we introduce a Depth-Guided (DG) SSIS framework. This framework uses depth maps extracted from input images, which represent individual instances with closely associated distance values, offering precise contours for distinct instances. Unlike R"},"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":"2406.17413","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T09:36:50Z","cross_cats_sorted":[],"title_canon_sha256":"c9fa50d18b0aa7af7ac87f49a61c9ef15e1136a98c72550db36c425259197c84","abstract_canon_sha256":"c9e1bfca8de070c144d119f4e9391cbbaaf385fefff139aa6df7fe02ff902528"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:34.698745Z","signature_b64":"iU4Pmo0kLkFiYfIrSxewQQemJVEJEt2ZjxpGvxUSTM1zjbcJix+6aAWCWJpLHy1bW93pbMIbkmJHxjguhTGJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36c3076fa212035e94cd4db6498e78a3fb4f54c8a9cf5202c24ab1f0e3788c17","last_reissued_at":"2026-07-05T08:36:34.698266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:34.698266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Depth-Guided Semi-Supervised Instance Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianghang Lin, Jie Hu, Liujuan Cao, Rongrong Ji, Xiawu Zheng, Xin Chen","submitted_at":"2024-06-25T09:36:50Z","abstract_excerpt":"Semi-Supervised Instance Segmentation (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled images to generate pseudo-labels. However, such a mechanism often introduces unstable noise, as a single instance can display multiple RGB values. To overcome this limitation, we introduce a Depth-Guided (DG) SSIS framework. This framework uses depth maps extracted from input images, which represent individual instances with closely associated distance values, offering precise contours for distinct instances. Unlike R"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17413","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/2406.17413/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":"2406.17413","created_at":"2026-07-05T08:36:34.698325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.17413v1","created_at":"2026-07-05T08:36:34.698325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17413","created_at":"2026-07-05T08:36:34.698325+00:00"},{"alias_kind":"pith_short_12","alias_value":"G3BQO35CCIBV","created_at":"2026-07-05T08:36:34.698325+00:00"},{"alias_kind":"pith_short_16","alias_value":"G3BQO35CCIBV5FGN","created_at":"2026-07-05T08:36:34.698325+00:00"},{"alias_kind":"pith_short_8","alias_value":"G3BQO35C","created_at":"2026-07-05T08:36:34.698325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.03841","citing_title":"Training a Student Expert via Semi-Supervised Foundation Model Distillation","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP","json":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP.json","graph_json":"https://pith.science/api/pith-number/G3BQO35CCIBV5FGNJW3ETDTYUP/graph.json","events_json":"https://pith.science/api/pith-number/G3BQO35CCIBV5FGNJW3ETDTYUP/events.json","paper":"https://pith.science/paper/G3BQO35C"},"agent_actions":{"view_html":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP","download_json":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP.json","view_paper":"https://pith.science/paper/G3BQO35C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.17413&json=true","fetch_graph":"https://pith.science/api/pith-number/G3BQO35CCIBV5FGNJW3ETDTYUP/graph.json","fetch_events":"https://pith.science/api/pith-number/G3BQO35CCIBV5FGNJW3ETDTYUP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP/action/storage_attestation","attest_author":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP/action/author_attestation","sign_citation":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP/action/citation_signature","submit_replication":"https://pith.science/pith/G3BQO35CCIBV5FGNJW3ETDTYUP/action/replication_record"}},"created_at":"2026-07-05T08:36:34.698325+00:00","updated_at":"2026-07-05T08:36:34.698325+00:00"}