{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BYEFWKXCBXB5TIACZN4N7QEYVP","short_pith_number":"pith:BYEFWKXC","canonical_record":{"source":{"id":"2312.08878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-12T08:49:39Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"9909e342d573c95747d5f037b9d36249fe23a408e4b2378af815b2ea6e45300a","abstract_canon_sha256":"250cfb77592e0441af2aa01b8d57800b50c90bae79929b2687e5236082bcfe98"},"schema_version":"1.0"},"canonical_sha256":"0e085b2ae20dc3d9a002cb78dfc098abdd4de2e11c0df72c539765508023ba39","source":{"kind":"arxiv","id":"2312.08878","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.08878","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"arxiv_version","alias_value":"2312.08878v1","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08878","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_12","alias_value":"BYEFWKXCBXB5","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_16","alias_value":"BYEFWKXCBXB5TIAC","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_8","alias_value":"BYEFWKXC","created_at":"2026-07-05T07:24:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BYEFWKXCBXB5TIACZN4N7QEYVP","target":"record","payload":{"canonical_record":{"source":{"id":"2312.08878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-12T08:49:39Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"9909e342d573c95747d5f037b9d36249fe23a408e4b2378af815b2ea6e45300a","abstract_canon_sha256":"250cfb77592e0441af2aa01b8d57800b50c90bae79929b2687e5236082bcfe98"},"schema_version":"1.0"},"canonical_sha256":"0e085b2ae20dc3d9a002cb78dfc098abdd4de2e11c0df72c539765508023ba39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:10.951021Z","signature_b64":"ixtm7IaGNGt8wSCdM/Zx8tE79+1u/2S9uRMSD32NmL5nLBqIVOLfmeusvDAatfy9wQnrpt2X7lUZzMqc0fMoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e085b2ae20dc3d9a002cb78dfc098abdd4de2e11c0df72c539765508023ba39","last_reissued_at":"2026-07-05T07:24:10.950668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:10.950668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.08878","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:24:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aap8y+jIxUepd20UlzHquZuPhWswIp6zYGwpaU8PWjjv7iGqTsCUkc7v7T4QhvX50pjfH+FnNT8piO7Q9epoBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T19:32:03.773946Z"},"content_sha256":"8cb86a985514f6cb3a623639aab6a7546947edc6aa9904e0dea1b67f4eb001fb","schema_version":"1.0","event_id":"sha256:8cb86a985514f6cb3a623639aab6a7546947edc6aa9904e0dea1b67f4eb001fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BYEFWKXCBXB5TIACZN4N7QEYVP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Domain Prompt Learning with Quaternion Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"cs.CV","authors_text":"Chao Ma, Qinglong Cao, Xiaokang Yang, Yuntian Chen, Zhengqin Xu","submitted_at":"2023-12-12T08:49:39Z","abstract_excerpt":"Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote sensing and medical imaging, domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge, their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this, we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08878","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/2312.08878/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:24:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ie6fD97H7u30A2K84PWZsM40+kKp3h/68plgPlJHB/ksprT9M6GZ8r2Uh4MUYlrlcs57+Cj2MT+Vib5eP+zEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T19:32:03.775018Z"},"content_sha256":"7853fb6ceb360e0cbaa1ee86a3f1486400d2bc113fe26c57bd2fbe039e2f03c1","schema_version":"1.0","event_id":"sha256:7853fb6ceb360e0cbaa1ee86a3f1486400d2bc113fe26c57bd2fbe039e2f03c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/bundle.json","state_url":"https://pith.science/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T19:32:03Z","links":{"resolver":"https://pith.science/pith/BYEFWKXCBXB5TIACZN4N7QEYVP","bundle":"https://pith.science/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/bundle.json","state":"https://pith.science/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BYEFWKXCBXB5TIACZN4N7QEYVP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BYEFWKXCBXB5TIACZN4N7QEYVP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"250cfb77592e0441af2aa01b8d57800b50c90bae79929b2687e5236082bcfe98","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-12T08:49:39Z","title_canon_sha256":"9909e342d573c95747d5f037b9d36249fe23a408e4b2378af815b2ea6e45300a"},"schema_version":"1.0","source":{"id":"2312.08878","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.08878","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"arxiv_version","alias_value":"2312.08878v1","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08878","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_12","alias_value":"BYEFWKXCBXB5","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_16","alias_value":"BYEFWKXCBXB5TIAC","created_at":"2026-07-05T07:24:10Z"},{"alias_kind":"pith_short_8","alias_value":"BYEFWKXC","created_at":"2026-07-05T07:24:10Z"}],"graph_snapshots":[{"event_id":"sha256:7853fb6ceb360e0cbaa1ee86a3f1486400d2bc113fe26c57bd2fbe039e2f03c1","target":"graph","created_at":"2026-07-05T07:24:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2312.08878/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote sensing and medical imaging, domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge, their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this, we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability ","authors_text":"Chao Ma, Qinglong Cao, Xiaokang Yang, Yuntian Chen, Zhengqin Xu","cross_cats":["cs.LG","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-12T08:49:39Z","title":"Domain Prompt Learning with Quaternion Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08878","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8cb86a985514f6cb3a623639aab6a7546947edc6aa9904e0dea1b67f4eb001fb","target":"record","created_at":"2026-07-05T07:24:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"250cfb77592e0441af2aa01b8d57800b50c90bae79929b2687e5236082bcfe98","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-12T08:49:39Z","title_canon_sha256":"9909e342d573c95747d5f037b9d36249fe23a408e4b2378af815b2ea6e45300a"},"schema_version":"1.0","source":{"id":"2312.08878","kind":"arxiv","version":1}},"canonical_sha256":"0e085b2ae20dc3d9a002cb78dfc098abdd4de2e11c0df72c539765508023ba39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e085b2ae20dc3d9a002cb78dfc098abdd4de2e11c0df72c539765508023ba39","first_computed_at":"2026-07-05T07:24:10.950668Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:24:10.950668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ixtm7IaGNGt8wSCdM/Zx8tE79+1u/2S9uRMSD32NmL5nLBqIVOLfmeusvDAatfy9wQnrpt2X7lUZzMqc0fMoCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:24:10.951021Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.08878","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8cb86a985514f6cb3a623639aab6a7546947edc6aa9904e0dea1b67f4eb001fb","sha256:7853fb6ceb360e0cbaa1ee86a3f1486400d2bc113fe26c57bd2fbe039e2f03c1"],"state_sha256":"281b032704c4e1c1b21dc85b19498b70a9e5201fb5ee583b728c7b58286c0580"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u+wzi2YbGmxdMsoU+Ci55bBHKAPcALc8nW6JoeC7RbIJaUYMHVOpyHzcL/sAQYAnM7DZCchLGR7PDb3hdlPZBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T19:32:03.781551Z","bundle_sha256":"f753b7569a7bf0515430cd6fb940afc57cdedfd93c412419bb4d77fd327642ba"}}