{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:E5FEYI6QTMM4V2S5466O5IXDFV","short_pith_number":"pith:E5FEYI6Q","schema_version":"1.0","canonical_sha256":"274a4c23d09b19caea5de7bceea2e32d6a3f77c5f73b47f2e1278fe70fb1f4a0","source":{"kind":"arxiv","id":"1803.08904","version":1},"attestation_state":"computed","paper":{"title":"Context Encoding for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ambrish Tyagi, Amit Agrawal, Hang Zhang, Jianping Shi, Kristin Dana, Xiaogang Wang, Zhongyue Zhang","submitted_at":"2018-03-23T17:34:21Z","abstract_excerpt":"Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra c"},"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":"1803.08904","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-23T17:34:21Z","cross_cats_sorted":[],"title_canon_sha256":"849b59788aa9c00d1ea5c385fa9eb29b531ce2a70cfb141f26779f24307d78b4","abstract_canon_sha256":"b0f9ac0e6bdaca05350859c66c9bf9f63e0d068bd353c91783d880e0483bdd1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:18.256455Z","signature_b64":"PCBJMqGD9Ax+5fSE0NLBfwtnImaJj60lEqlq0zSROJL6wO/dp6cdpIl6twf2uW5BogKtitUtCmjwOPuYArLLCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"274a4c23d09b19caea5de7bceea2e32d6a3f77c5f73b47f2e1278fe70fb1f4a0","last_reissued_at":"2026-05-18T00:20:18.255764Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:18.255764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Context Encoding for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ambrish Tyagi, Amit Agrawal, Hang Zhang, Jianping Shi, Kristin Dana, Xiaogang Wang, Zhongyue Zhang","submitted_at":"2018-03-23T17:34:21Z","abstract_excerpt":"Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.08904","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":"1803.08904","created_at":"2026-05-18T00:20:18.255859+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.08904v1","created_at":"2026-05-18T00:20:18.255859+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.08904","created_at":"2026-05-18T00:20:18.255859+00:00"},{"alias_kind":"pith_short_12","alias_value":"E5FEYI6QTMM4","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"E5FEYI6QTMM4V2S5","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"E5FEYI6Q","created_at":"2026-05-18T12:32:19.392346+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/E5FEYI6QTMM4V2S5466O5IXDFV","json":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV.json","graph_json":"https://pith.science/api/pith-number/E5FEYI6QTMM4V2S5466O5IXDFV/graph.json","events_json":"https://pith.science/api/pith-number/E5FEYI6QTMM4V2S5466O5IXDFV/events.json","paper":"https://pith.science/paper/E5FEYI6Q"},"agent_actions":{"view_html":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV","download_json":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV.json","view_paper":"https://pith.science/paper/E5FEYI6Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.08904&json=true","fetch_graph":"https://pith.science/api/pith-number/E5FEYI6QTMM4V2S5466O5IXDFV/graph.json","fetch_events":"https://pith.science/api/pith-number/E5FEYI6QTMM4V2S5466O5IXDFV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV/action/storage_attestation","attest_author":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV/action/author_attestation","sign_citation":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV/action/citation_signature","submit_replication":"https://pith.science/pith/E5FEYI6QTMM4V2S5466O5IXDFV/action/replication_record"}},"created_at":"2026-05-18T00:20:18.255859+00:00","updated_at":"2026-05-18T00:20:18.255859+00:00"}