{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BUEOCKLCSDYUCG7PLAHVBJSZ4R","short_pith_number":"pith:BUEOCKLC","schema_version":"1.0","canonical_sha256":"0d08e1296290f1411bef580f50a659e47efda386c19dbc62376dcc60b6273cb0","source":{"kind":"arxiv","id":"2208.11608","version":1},"attestation_state":"computed","paper":{"title":"Sliding Window Recurrent Network for Efficient Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Wenjing Lian, Wenyi Lian","submitted_at":"2022-08-24T15:23:44Z","abstract_excerpt":"Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. Different from single image super-resolution, VSR can utilize frames' temporal information to reconstruct results with more details. Recently, with the rapid development of convolution neural networks (CNN), the VSR task has drawn increasing attention and many CNN-based methods have achieved remarkable results. However, only a few VSR approaches can be applied to real-world mobile devices due to the computational resources and runtime limitations. In this paper, we propose a \\"},"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":"2208.11608","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-08-24T15:23:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0563933157c369232fe872c34c25ffcfaea76cf852f8af7dfbf035da5d1caecb","abstract_canon_sha256":"ff1df4e47334e19ac75dc4c9e44ab50d47e670ded2fc7cd9301395730b443485"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:51:16.949626Z","signature_b64":"6R5MgZQsYeOI+669OoZap4AMMygHpCeRvLFlaiHvUX/M9/q0R6HU+rK4kgK3m8RzqMg9m4bOzYxL3/Te+CgVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d08e1296290f1411bef580f50a659e47efda386c19dbc62376dcc60b6273cb0","last_reissued_at":"2026-07-05T04:51:16.949238Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:51:16.949238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sliding Window Recurrent Network for Efficient Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Wenjing Lian, Wenyi Lian","submitted_at":"2022-08-24T15:23:44Z","abstract_excerpt":"Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. Different from single image super-resolution, VSR can utilize frames' temporal information to reconstruct results with more details. Recently, with the rapid development of convolution neural networks (CNN), the VSR task has drawn increasing attention and many CNN-based methods have achieved remarkable results. However, only a few VSR approaches can be applied to real-world mobile devices due to the computational resources and runtime limitations. In this paper, we propose a \\"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11608","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/2208.11608/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":"2208.11608","created_at":"2026-07-05T04:51:16.949307+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.11608v1","created_at":"2026-07-05T04:51:16.949307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11608","created_at":"2026-07-05T04:51:16.949307+00:00"},{"alias_kind":"pith_short_12","alias_value":"BUEOCKLCSDYU","created_at":"2026-07-05T04:51:16.949307+00:00"},{"alias_kind":"pith_short_16","alias_value":"BUEOCKLCSDYUCG7P","created_at":"2026-07-05T04:51:16.949307+00:00"},{"alias_kind":"pith_short_8","alias_value":"BUEOCKLC","created_at":"2026-07-05T04:51:16.949307+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/BUEOCKLCSDYUCG7PLAHVBJSZ4R","json":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R.json","graph_json":"https://pith.science/api/pith-number/BUEOCKLCSDYUCG7PLAHVBJSZ4R/graph.json","events_json":"https://pith.science/api/pith-number/BUEOCKLCSDYUCG7PLAHVBJSZ4R/events.json","paper":"https://pith.science/paper/BUEOCKLC"},"agent_actions":{"view_html":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R","download_json":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R.json","view_paper":"https://pith.science/paper/BUEOCKLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.11608&json=true","fetch_graph":"https://pith.science/api/pith-number/BUEOCKLCSDYUCG7PLAHVBJSZ4R/graph.json","fetch_events":"https://pith.science/api/pith-number/BUEOCKLCSDYUCG7PLAHVBJSZ4R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R/action/storage_attestation","attest_author":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R/action/author_attestation","sign_citation":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R/action/citation_signature","submit_replication":"https://pith.science/pith/BUEOCKLCSDYUCG7PLAHVBJSZ4R/action/replication_record"}},"created_at":"2026-07-05T04:51:16.949307+00:00","updated_at":"2026-07-05T04:51:16.949307+00:00"}