{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JQK2N2RJWUYYBBTLMHD2GSCPXO","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":"6e15639ed7542bd2f443ead44d172652ca7b67b56dba2e61a7bc89d65ec7cde2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-18T11:11:10Z","title_canon_sha256":"44057782b90c5af34db72e7ddcb581e009766aeb15a527ee9fb1ad9b3b905e7c"},"schema_version":"1.0","source":{"id":"1803.06641","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.06641","created_at":"2026-05-18T00:20:45Z"},{"alias_kind":"arxiv_version","alias_value":"1803.06641v1","created_at":"2026-05-18T00:20:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.06641","created_at":"2026-05-18T00:20:45Z"},{"alias_kind":"pith_short_12","alias_value":"JQK2N2RJWUYY","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JQK2N2RJWUYYBBTL","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JQK2N2RJ","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:5fb0d33833da892b5a08757b30111e83f36719bbcd8ac3800be15c1ffc54541d","target":"graph","created_at":"2026-05-18T00:20:45Z","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"},"paper":{"abstract_excerpt":"Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major difficulty is to collect accurate ground-truth disparities for stereo pairs in the target domain. In this work, we propose a self-adaptation approach for CNN training, utilizing both synthetic training data (with ground-truth disparities) and stereo pairs in the new domain (without ground-truths). Our method is driven by two empirical observations. By feeding real stereo pairs of different domains to stereo models p","authors_text":"Chengxi Yang, Jiahao Pang, Jimmy Ren, Jin Zeng, Liang Lin, Ruichao Xiao, Wenxiu Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-18T11:11:10Z","title":"Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06641","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":"sha256:790d25bea8ece5ac88ac9a8e95aaf8e833be9d2fad0e2a0b6cd00cc4c08f72e6","target":"record","created_at":"2026-05-18T00:20:45Z","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":"6e15639ed7542bd2f443ead44d172652ca7b67b56dba2e61a7bc89d65ec7cde2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-18T11:11:10Z","title_canon_sha256":"44057782b90c5af34db72e7ddcb581e009766aeb15a527ee9fb1ad9b3b905e7c"},"schema_version":"1.0","source":{"id":"1803.06641","kind":"arxiv","version":1}},"canonical_sha256":"4c15a6ea29b53180866b61c7a3484fbb907b705c742cf96767216004b1c176ad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c15a6ea29b53180866b61c7a3484fbb907b705c742cf96767216004b1c176ad","first_computed_at":"2026-05-18T00:20:45.409992Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:45.409992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bgRXmT9Z6r87m1yDe4t/Mt66Mp8KNaEPeKT0BJzPUQgmJmNe32bnr8gebDIdkMR4p5JVy4brZBhgrFsI5mjvDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:45.410466Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.06641","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:790d25bea8ece5ac88ac9a8e95aaf8e833be9d2fad0e2a0b6cd00cc4c08f72e6","sha256:5fb0d33833da892b5a08757b30111e83f36719bbcd8ac3800be15c1ffc54541d"],"state_sha256":"82dccff6ac5ce1c182a7cea80fa56dfdb9e94228311ff82663370be31943b82b"}