{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OPVZDWIN3P32BLEZUBCSBB4TIE","short_pith_number":"pith:OPVZDWIN","schema_version":"1.0","canonical_sha256":"73eb91d90ddbf7a0ac99a045208793412cdf21d8c321807e37d221b8f375c7f9","source":{"kind":"arxiv","id":"2506.07376","version":1},"attestation_state":"computed","paper":{"title":"Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangyao Chen, Jintao Tong, Ran Ma, Ruixuan Li, Yixiong Zou, Yuhua Li","submitted_at":"2025-06-09T02:51:06Z","abstract_excerpt":"Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few samples are available for efficient fine-tuning. There are majorly two challenges in this task: (1) the domain gap and (2) fine-tuning with scarce data. To solve these challenges, we revisit the adapter-based methods, and discover an intriguing insight not explored in previous works: the adapter not only helps the fine-tuning of downstream tasks but also naturally serves as a domain information "},"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":"2506.07376","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T02:51:06Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5218cdf4960d83b95e9d861ed6009ae97d64350045de7e41432d1a64e6d11402","abstract_canon_sha256":"2e6c5f9ddc125c073cc05756ae51d06c78edf2b9baf52c3fe816f17fc84d03d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:26.109743Z","signature_b64":"62lRdbnUcuEZhK5j7Rupy2REadePzzuTr4Gc/+T7LKpZes5mIKiZ/MJFWTIBtbwIDuD68trik6EZiU9TNBNuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73eb91d90ddbf7a0ac99a045208793412cdf21d8c321807e37d221b8f375c7f9","last_reissued_at":"2026-07-05T11:18:26.109071Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:26.109071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangyao Chen, Jintao Tong, Ran Ma, Ruixuan Li, Yixiong Zou, Yuhua Li","submitted_at":"2025-06-09T02:51:06Z","abstract_excerpt":"Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few samples are available for efficient fine-tuning. There are majorly two challenges in this task: (1) the domain gap and (2) fine-tuning with scarce data. To solve these challenges, we revisit the adapter-based methods, and discover an intriguing insight not explored in previous works: the adapter not only helps the fine-tuning of downstream tasks but also naturally serves as a domain information "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07376","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/2506.07376/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":"2506.07376","created_at":"2026-07-05T11:18:26.109159+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.07376v1","created_at":"2026-07-05T11:18:26.109159+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07376","created_at":"2026-07-05T11:18:26.109159+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPVZDWIN3P32","created_at":"2026-07-05T11:18:26.109159+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPVZDWIN3P32BLEZ","created_at":"2026-07-05T11:18:26.109159+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPVZDWIN","created_at":"2026-07-05T11:18:26.109159+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24296","citing_title":"Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19340","citing_title":"Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE","json":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE.json","graph_json":"https://pith.science/api/pith-number/OPVZDWIN3P32BLEZUBCSBB4TIE/graph.json","events_json":"https://pith.science/api/pith-number/OPVZDWIN3P32BLEZUBCSBB4TIE/events.json","paper":"https://pith.science/paper/OPVZDWIN"},"agent_actions":{"view_html":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE","download_json":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE.json","view_paper":"https://pith.science/paper/OPVZDWIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.07376&json=true","fetch_graph":"https://pith.science/api/pith-number/OPVZDWIN3P32BLEZUBCSBB4TIE/graph.json","fetch_events":"https://pith.science/api/pith-number/OPVZDWIN3P32BLEZUBCSBB4TIE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE/action/storage_attestation","attest_author":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE/action/author_attestation","sign_citation":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE/action/citation_signature","submit_replication":"https://pith.science/pith/OPVZDWIN3P32BLEZUBCSBB4TIE/action/replication_record"}},"created_at":"2026-07-05T11:18:26.109159+00:00","updated_at":"2026-07-05T11:18:26.109159+00:00"}