{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:D6A6UVEAN2JCCHIJFJVW77KU6M","short_pith_number":"pith:D6A6UVEA","schema_version":"1.0","canonical_sha256":"1f81ea54806e92211d092a6b6ffd54f33962542949c54f80d471b3825cfd8283","source":{"kind":"arxiv","id":"2407.13500","version":1},"attestation_state":"computed","paper":{"title":"FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Lu, Hongtao Fu, Wenze Liu, Zhiguo Cao","submitted_at":"2024-07-18T13:32:36Z","abstract_excerpt":"The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail-sensitive tasks such as image matting. Prior upsampling operators often can work well in either type of the tasks, but not both. We argue that task-agnostic upsampling should dynamically trade off between semantic preservation and detail delineation, instead of having a bias between the two properties. In this paper, we present FADE, a novel, plug-and-play, lightweight, and ta"},"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":"2407.13500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-18T13:32:36Z","cross_cats_sorted":[],"title_canon_sha256":"305b68bfa11625492e9b8bcc4a8cc78c2830b4037924a9097b49de4332e5dcbe","abstract_canon_sha256":"e10e7a8ba21f41f5e3986db6a1f56b81cf7fff8c9a1f2a6c451aa383c678ec9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:45:39.815651Z","signature_b64":"thPZvsD5h6pm00su8Zd+sKkjwQGPqzL1cQZuqXPVeUL6bs704BjgsrkQKTCcV9i4s3ChDqrEphWE0UKSsGMEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f81ea54806e92211d092a6b6ffd54f33962542949c54f80d471b3825cfd8283","last_reissued_at":"2026-07-05T08:45:39.815209Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:45:39.815209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Lu, Hongtao Fu, Wenze Liu, Zhiguo Cao","submitted_at":"2024-07-18T13:32:36Z","abstract_excerpt":"The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail-sensitive tasks such as image matting. Prior upsampling operators often can work well in either type of the tasks, but not both. We argue that task-agnostic upsampling should dynamically trade off between semantic preservation and detail delineation, instead of having a bias between the two properties. In this paper, we present FADE, a novel, plug-and-play, lightweight, and ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.13500","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/2407.13500/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":"2407.13500","created_at":"2026-07-05T08:45:39.815264+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.13500v1","created_at":"2026-07-05T08:45:39.815264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.13500","created_at":"2026-07-05T08:45:39.815264+00:00"},{"alias_kind":"pith_short_12","alias_value":"D6A6UVEAN2JC","created_at":"2026-07-05T08:45:39.815264+00:00"},{"alias_kind":"pith_short_16","alias_value":"D6A6UVEAN2JCCHIJ","created_at":"2026-07-05T08:45:39.815264+00:00"},{"alias_kind":"pith_short_8","alias_value":"D6A6UVEA","created_at":"2026-07-05T08:45:39.815264+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/D6A6UVEAN2JCCHIJFJVW77KU6M","json":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M.json","graph_json":"https://pith.science/api/pith-number/D6A6UVEAN2JCCHIJFJVW77KU6M/graph.json","events_json":"https://pith.science/api/pith-number/D6A6UVEAN2JCCHIJFJVW77KU6M/events.json","paper":"https://pith.science/paper/D6A6UVEA"},"agent_actions":{"view_html":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M","download_json":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M.json","view_paper":"https://pith.science/paper/D6A6UVEA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.13500&json=true","fetch_graph":"https://pith.science/api/pith-number/D6A6UVEAN2JCCHIJFJVW77KU6M/graph.json","fetch_events":"https://pith.science/api/pith-number/D6A6UVEAN2JCCHIJFJVW77KU6M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M/action/storage_attestation","attest_author":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M/action/author_attestation","sign_citation":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M/action/citation_signature","submit_replication":"https://pith.science/pith/D6A6UVEAN2JCCHIJFJVW77KU6M/action/replication_record"}},"created_at":"2026-07-05T08:45:39.815264+00:00","updated_at":"2026-07-05T08:45:39.815264+00:00"}