{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XNB2MBEFPMXO626QFLTXAOUDSU","short_pith_number":"pith:XNB2MBEF","schema_version":"1.0","canonical_sha256":"bb43a604857b2eef6bd02ae7703a83952ac64e8546a7b5f175b9b4ff00d6cab7","source":{"kind":"arxiv","id":"1704.06857","version":1},"attestation_state":"computed","paper":{"title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alberto Garcia-Garcia, Jose Garcia-Rodriguez, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez","submitted_at":"2017-04-22T23:37:43Z","abstract_excerpt":"Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various applicati"},"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":"1704.06857","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-22T23:37:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b38d522240ef416b35a87e935f7817e20d3090ffc7062aface913e33d6950798","abstract_canon_sha256":"a704aee99c3b15243ccd56b426b571aded1b7ca86eddd4217d25c7a85e9dc0fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:55.568505Z","signature_b64":"FZyexYaFYT+8BzRIhOtAetjdKmXTj0A2c2R9kqmquS3/ExU1aM8sUDnW3Agq/csBKhqHVh1g6BQxX/E+F809Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb43a604857b2eef6bd02ae7703a83952ac64e8546a7b5f175b9b4ff00d6cab7","last_reissued_at":"2026-05-18T00:45:55.568076Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:55.568076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alberto Garcia-Garcia, Jose Garcia-Rodriguez, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez","submitted_at":"2017-04-22T23:37:43Z","abstract_excerpt":"Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various applicati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06857","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":"1704.06857","created_at":"2026-05-18T00:45:55.568147+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.06857v1","created_at":"2026-05-18T00:45:55.568147+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06857","created_at":"2026-05-18T00:45:55.568147+00:00"},{"alias_kind":"pith_short_12","alias_value":"XNB2MBEFPMXO","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XNB2MBEFPMXO626Q","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XNB2MBEF","created_at":"2026-05-18T12:31:56.362134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.02149","citing_title":"Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"1907.06119","citing_title":"Understanding Deep Learning Techniques for Image Segmentation","ref_index":66,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03413","citing_title":"Learning to Theorize the World from Observation","ref_index":119,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08893","citing_title":"Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05431","citing_title":"Cross-Stage Attention Propagation for Efficient Semantic Segmentation","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU","json":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU.json","graph_json":"https://pith.science/api/pith-number/XNB2MBEFPMXO626QFLTXAOUDSU/graph.json","events_json":"https://pith.science/api/pith-number/XNB2MBEFPMXO626QFLTXAOUDSU/events.json","paper":"https://pith.science/paper/XNB2MBEF"},"agent_actions":{"view_html":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU","download_json":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU.json","view_paper":"https://pith.science/paper/XNB2MBEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.06857&json=true","fetch_graph":"https://pith.science/api/pith-number/XNB2MBEFPMXO626QFLTXAOUDSU/graph.json","fetch_events":"https://pith.science/api/pith-number/XNB2MBEFPMXO626QFLTXAOUDSU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU/action/storage_attestation","attest_author":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU/action/author_attestation","sign_citation":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU/action/citation_signature","submit_replication":"https://pith.science/pith/XNB2MBEFPMXO626QFLTXAOUDSU/action/replication_record"}},"created_at":"2026-05-18T00:45:55.568147+00:00","updated_at":"2026-05-18T00:45:55.568147+00:00"}