{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ICUOYMN77KZCNLLKZF7E4ENXRG","short_pith_number":"pith:ICUOYMN7","schema_version":"1.0","canonical_sha256":"40a8ec31bffab226ad6ac97e4e11b789be2121ebc545b3f0a2c045f6ffddce54","source":{"kind":"arxiv","id":"2312.00360","version":2},"attestation_state":"computed","paper":{"title":"Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongfang Liu, Heng Fan, Qing Yang, Shaohua Dong, Yan Huang, Yunhe Feng","submitted_at":"2023-12-01T05:50:44Z","abstract_excerpt":"Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion. To address this issue, we propose a surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal (e.g., RGB-D/T) s"},"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":"2312.00360","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T05:50:44Z","cross_cats_sorted":[],"title_canon_sha256":"6f434d15c8f406946974d4d314de73040be83634cb442eaab18acb0e84b31569","abstract_canon_sha256":"56f7cd6caf1590d1139d55785b441d3215ac0c788ab364aa22a0608876fdfc2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:54.238612Z","signature_b64":"ZFS6xCzQqz1E5P2pkuYWgUKS69Jfar9fJ3BmKIcWhliEDHi/5Ln420mEjunbBnAfM0D0/QXX5nlJMnAwXXYTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40a8ec31bffab226ad6ac97e4e11b789be2121ebc545b3f0a2c045f6ffddce54","last_reissued_at":"2026-07-05T07:19:54.238129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:54.238129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongfang Liu, Heng Fan, Qing Yang, Shaohua Dong, Yan Huang, Yunhe Feng","submitted_at":"2023-12-01T05:50:44Z","abstract_excerpt":"Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion. To address this issue, we propose a surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal (e.g., RGB-D/T) s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00360","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/2312.00360/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":"2312.00360","created_at":"2026-07-05T07:19:54.238186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.00360v2","created_at":"2026-07-05T07:19:54.238186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00360","created_at":"2026-07-05T07:19:54.238186+00:00"},{"alias_kind":"pith_short_12","alias_value":"ICUOYMN77KZC","created_at":"2026-07-05T07:19:54.238186+00:00"},{"alias_kind":"pith_short_16","alias_value":"ICUOYMN77KZCNLLK","created_at":"2026-07-05T07:19:54.238186+00:00"},{"alias_kind":"pith_short_8","alias_value":"ICUOYMN7","created_at":"2026-07-05T07:19:54.238186+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/ICUOYMN77KZCNLLKZF7E4ENXRG","json":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG.json","graph_json":"https://pith.science/api/pith-number/ICUOYMN77KZCNLLKZF7E4ENXRG/graph.json","events_json":"https://pith.science/api/pith-number/ICUOYMN77KZCNLLKZF7E4ENXRG/events.json","paper":"https://pith.science/paper/ICUOYMN7"},"agent_actions":{"view_html":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG","download_json":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG.json","view_paper":"https://pith.science/paper/ICUOYMN7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.00360&json=true","fetch_graph":"https://pith.science/api/pith-number/ICUOYMN77KZCNLLKZF7E4ENXRG/graph.json","fetch_events":"https://pith.science/api/pith-number/ICUOYMN77KZCNLLKZF7E4ENXRG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG/action/storage_attestation","attest_author":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG/action/author_attestation","sign_citation":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG/action/citation_signature","submit_replication":"https://pith.science/pith/ICUOYMN77KZCNLLKZF7E4ENXRG/action/replication_record"}},"created_at":"2026-07-05T07:19:54.238186+00:00","updated_at":"2026-07-05T07:19:54.238186+00:00"}