{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VBHAWNPRC5GS4XBQVSJKGNKHYF","short_pith_number":"pith:VBHAWNPR","schema_version":"1.0","canonical_sha256":"a84e0b35f1174d2e5c30ac92a33547c15670d23da5e433193584214ba8aebd56","source":{"kind":"arxiv","id":"1807.03959","version":1},"attestation_state":"computed","paper":{"title":"Deep attention-based classification network for robust depth prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Hao Lu, Ke Xian, Lingxiao Hang, Ruibo Li, Zhiguo Cao","submitted_at":"2018-07-11T06:19:22Z","abstract_excerpt":"In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)? b) How to handle the large differences of depth ranges between indoor and outdoor datasets?"},"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":"1807.03959","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-11T06:19:22Z","cross_cats_sorted":[],"title_canon_sha256":"4a7d685bf8c19a1ce818b027f12b04be08284b75ca4153d0927dd6adafa1387f","abstract_canon_sha256":"235018a105ecf218f5852de6c918ca78080fcf6639e23bc37112908507d75082"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:58.497221Z","signature_b64":"u8UcvdRcxsHhPJ+KR74OPMLX3I7+XGc7z0ChwtxAamI0W2uv9oT2m2ofW+fzj+zubUq8NQmeYlX9pbXBeeMQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a84e0b35f1174d2e5c30ac92a33547c15670d23da5e433193584214ba8aebd56","last_reissued_at":"2026-05-18T00:10:58.496452Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:58.496452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep attention-based classification network for robust depth prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Hao Lu, Ke Xian, Lingxiao Hang, Ruibo Li, Zhiguo Cao","submitted_at":"2018-07-11T06:19:22Z","abstract_excerpt":"In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)? b) How to handle the large differences of depth ranges between indoor and outdoor datasets?"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03959","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":""},"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":"1807.03959","created_at":"2026-05-18T00:10:58.496570+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.03959v1","created_at":"2026-05-18T00:10:58.496570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03959","created_at":"2026-05-18T00:10:58.496570+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBHAWNPRC5GS","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBHAWNPRC5GS4XBQ","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBHAWNPR","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.10659","citing_title":"SDNet: Semantically Guided Depth Estimation Network","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF","json":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF.json","graph_json":"https://pith.science/api/pith-number/VBHAWNPRC5GS4XBQVSJKGNKHYF/graph.json","events_json":"https://pith.science/api/pith-number/VBHAWNPRC5GS4XBQVSJKGNKHYF/events.json","paper":"https://pith.science/paper/VBHAWNPR"},"agent_actions":{"view_html":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF","download_json":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF.json","view_paper":"https://pith.science/paper/VBHAWNPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.03959&json=true","fetch_graph":"https://pith.science/api/pith-number/VBHAWNPRC5GS4XBQVSJKGNKHYF/graph.json","fetch_events":"https://pith.science/api/pith-number/VBHAWNPRC5GS4XBQVSJKGNKHYF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF/action/storage_attestation","attest_author":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF/action/author_attestation","sign_citation":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF/action/citation_signature","submit_replication":"https://pith.science/pith/VBHAWNPRC5GS4XBQVSJKGNKHYF/action/replication_record"}},"created_at":"2026-05-18T00:10:58.496570+00:00","updated_at":"2026-05-18T00:10:58.496570+00:00"}