{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:4LUFIVFHMAIABXE3ZWCZELPGP3","short_pith_number":"pith:4LUFIVFH","schema_version":"1.0","canonical_sha256":"e2e85454a7601000dc9bcd85922de67ed19449eebe4960f1a0d84d41ed7ce540","source":{"kind":"arxiv","id":"2011.14288","version":1},"attestation_state":"computed","paper":{"title":"Learning Affinity-Aware Upsampling for Deep Image Matting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Hao Lu, Yutong Dai","submitted_at":"2020-11-29T05:09:43Z","abstract_excerpt":"We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is essential, learning affinity in upsampling can avoid additional propagation layers, offering the potential for building compact models. By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affin"},"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":"2011.14288","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-29T05:09:43Z","cross_cats_sorted":[],"title_canon_sha256":"8a69c5218c7c9b413d3258e07fc74c6c99ccb8c3918bd9fef5abe6e39e49d97b","abstract_canon_sha256":"777e521c15be0d9f2b130ad87f39f25e1869c675e86c8dd792d188e3bceaa906"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:19.334456Z","signature_b64":"BJe6eHKXknXQuxGSgP2K2r8c1wPAw5+HuagW3s9lKk/TuQJ8pC9aYIbljCy5E1HJf1157w2nbRRP2E5waMb5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2e85454a7601000dc9bcd85922de67ed19449eebe4960f1a0d84d41ed7ce540","last_reissued_at":"2026-07-05T01:55:19.333882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:19.333882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Affinity-Aware Upsampling for Deep Image Matting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Hao Lu, Yutong Dai","submitted_at":"2020-11-29T05:09:43Z","abstract_excerpt":"We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is essential, learning affinity in upsampling can avoid additional propagation layers, offering the potential for building compact models. By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.14288","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/2011.14288/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":"2011.14288","created_at":"2026-07-05T01:55:19.333954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.14288v1","created_at":"2026-07-05T01:55:19.333954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.14288","created_at":"2026-07-05T01:55:19.333954+00:00"},{"alias_kind":"pith_short_12","alias_value":"4LUFIVFHMAIA","created_at":"2026-07-05T01:55:19.333954+00:00"},{"alias_kind":"pith_short_16","alias_value":"4LUFIVFHMAIABXE3","created_at":"2026-07-05T01:55:19.333954+00:00"},{"alias_kind":"pith_short_8","alias_value":"4LUFIVFH","created_at":"2026-07-05T01:55:19.333954+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/4LUFIVFHMAIABXE3ZWCZELPGP3","json":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3.json","graph_json":"https://pith.science/api/pith-number/4LUFIVFHMAIABXE3ZWCZELPGP3/graph.json","events_json":"https://pith.science/api/pith-number/4LUFIVFHMAIABXE3ZWCZELPGP3/events.json","paper":"https://pith.science/paper/4LUFIVFH"},"agent_actions":{"view_html":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3","download_json":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3.json","view_paper":"https://pith.science/paper/4LUFIVFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.14288&json=true","fetch_graph":"https://pith.science/api/pith-number/4LUFIVFHMAIABXE3ZWCZELPGP3/graph.json","fetch_events":"https://pith.science/api/pith-number/4LUFIVFHMAIABXE3ZWCZELPGP3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3/action/storage_attestation","attest_author":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3/action/author_attestation","sign_citation":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3/action/citation_signature","submit_replication":"https://pith.science/pith/4LUFIVFHMAIABXE3ZWCZELPGP3/action/replication_record"}},"created_at":"2026-07-05T01:55:19.333954+00:00","updated_at":"2026-07-05T01:55:19.333954+00:00"}