{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZHKUX6WDXHAAAAQOCLAJWM3Z4X","short_pith_number":"pith:ZHKUX6WD","schema_version":"1.0","canonical_sha256":"c9d54bfac3b9c000020e12c09b3379e5c47caf2857320e6f9ed56fd9d5728d98","source":{"kind":"arxiv","id":"2405.16419","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bryan A. Plummer, Chau Pham","submitted_at":"2024-05-26T03:41:40Z","abstract_excerpt":"Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source. Thus, MCI models must support a variety of channel configurations at test time. Recent work has extended traditional visual encoders for MCI, such as Vision Transformers (ViT), by supplementing pixel information with an encoding representing the channel configuration. However, these methods treat each channel "},"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":"2405.16419","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-26T03:41:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b4f557c0b465b163fbc6a5cff89c6aaa200ae0597f8efee4d145fc94f6d96168","abstract_canon_sha256":"bb4349dae04c8a9289f93ce0d01bfe36a512ba09d416c20eb601df12ceb9523a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:01.091675Z","signature_b64":"MLe2AblEorK81b9eTXsiMH7DYg4KWd+lOyT7g/5Wuhb+jsdSmJw7FMlODqNXN7kV7pa7FRXcq9H1Lxg4otThDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9d54bfac3b9c000020e12c09b3379e5c47caf2857320e6f9ed56fd9d5728d98","last_reissued_at":"2026-07-05T09:27:01.091242Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:01.091242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bryan A. Plummer, Chau Pham","submitted_at":"2024-05-26T03:41:40Z","abstract_excerpt":"Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source. Thus, MCI models must support a variety of channel configurations at test time. Recent work has extended traditional visual encoders for MCI, such as Vision Transformers (ViT), by supplementing pixel information with an encoding representing the channel configuration. However, these methods treat each channel "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16419","kind":"arxiv","version":2},"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/2405.16419/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":"2405.16419","created_at":"2026-07-05T09:27:01.091297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.16419v2","created_at":"2026-07-05T09:27:01.091297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16419","created_at":"2026-07-05T09:27:01.091297+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHKUX6WDXHAA","created_at":"2026-07-05T09:27:01.091297+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHKUX6WDXHAAAAQO","created_at":"2026-07-05T09:27:01.091297+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHKUX6WD","created_at":"2026-07-05T09:27:01.091297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.00594","citing_title":"Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing","ref_index":48,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X","json":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X.json","graph_json":"https://pith.science/api/pith-number/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/graph.json","events_json":"https://pith.science/api/pith-number/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/events.json","paper":"https://pith.science/paper/ZHKUX6WD"},"agent_actions":{"view_html":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X","download_json":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X.json","view_paper":"https://pith.science/paper/ZHKUX6WD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.16419&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/action/storage_attestation","attest_author":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/action/author_attestation","sign_citation":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/action/citation_signature","submit_replication":"https://pith.science/pith/ZHKUX6WDXHAAAAQOCLAJWM3Z4X/action/replication_record"}},"created_at":"2026-07-05T09:27:01.091297+00:00","updated_at":"2026-07-05T09:27:01.091297+00:00"}