{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H7WRB6W2TINJJCLLJU6BVXSV5C","short_pith_number":"pith:H7WRB6W2","schema_version":"1.0","canonical_sha256":"3fed10fada9a1a94896b4d3c1ade55e895918dca288e9f758ef659eb77942352","source":{"kind":"arxiv","id":"2403.08755","version":2},"attestation_state":"computed","paper":{"title":"DAM: Dynamic Adapter Merging for Continual Video QA Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Feng Cheng, Gedas Bertasius, Mohit Bansal, Yan-Bo Lin, Yi-Lin Sung, Ziyang Wang","submitted_at":"2024-03-13T17:53:47Z","abstract_excerpt":"We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses the proposed Dynamic Adapter Merging to (i) mitigate catastrophic forgetting, (ii) enable efficient adaptation to continually arriving datasets, (iii) handle inputs from unknown datasets during inference, and (iv) enable knowledge sharing across similar dataset domains. Given a set of continually streaming VidQA datasets, we sequentially train dataset-specific adapters for each dataset while freezing the parameters of a large pretrained video-language backbone. During in"},"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":"2403.08755","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-13T17:53:47Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"47a708a5e47a310dc2870faf7dc47564122ab7fd0c5bf22cd00341ad0590402d","abstract_canon_sha256":"5f6ca98d62e77f44f100df9e5154a5ad3bddff924041b1d54bf092ce4853a99e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:02.453557Z","signature_b64":"Tw0YcqxCX2bEIKxOKBilExo1sMszMvklkU+VkPgRBUuVWm4kTGIL/M3Zih6db0Wg2OAWw8gHTLyZ00LnhclkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fed10fada9a1a94896b4d3c1ade55e895918dca288e9f758ef659eb77942352","last_reissued_at":"2026-07-05T08:11:02.453081Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:02.453081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DAM: Dynamic Adapter Merging for Continual Video QA Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Feng Cheng, Gedas Bertasius, Mohit Bansal, Yan-Bo Lin, Yi-Lin Sung, Ziyang Wang","submitted_at":"2024-03-13T17:53:47Z","abstract_excerpt":"We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses the proposed Dynamic Adapter Merging to (i) mitigate catastrophic forgetting, (ii) enable efficient adaptation to continually arriving datasets, (iii) handle inputs from unknown datasets during inference, and (iv) enable knowledge sharing across similar dataset domains. Given a set of continually streaming VidQA datasets, we sequentially train dataset-specific adapters for each dataset while freezing the parameters of a large pretrained video-language backbone. During in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.08755","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/2403.08755/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":"2403.08755","created_at":"2026-07-05T08:11:02.453138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.08755v2","created_at":"2026-07-05T08:11:02.453138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.08755","created_at":"2026-07-05T08:11:02.453138+00:00"},{"alias_kind":"pith_short_12","alias_value":"H7WRB6W2TINJ","created_at":"2026-07-05T08:11:02.453138+00:00"},{"alias_kind":"pith_short_16","alias_value":"H7WRB6W2TINJJCLL","created_at":"2026-07-05T08:11:02.453138+00:00"},{"alias_kind":"pith_short_8","alias_value":"H7WRB6W2","created_at":"2026-07-05T08:11:02.453138+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.07666","citing_title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C","json":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C.json","graph_json":"https://pith.science/api/pith-number/H7WRB6W2TINJJCLLJU6BVXSV5C/graph.json","events_json":"https://pith.science/api/pith-number/H7WRB6W2TINJJCLLJU6BVXSV5C/events.json","paper":"https://pith.science/paper/H7WRB6W2"},"agent_actions":{"view_html":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C","download_json":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C.json","view_paper":"https://pith.science/paper/H7WRB6W2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.08755&json=true","fetch_graph":"https://pith.science/api/pith-number/H7WRB6W2TINJJCLLJU6BVXSV5C/graph.json","fetch_events":"https://pith.science/api/pith-number/H7WRB6W2TINJJCLLJU6BVXSV5C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C/action/storage_attestation","attest_author":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C/action/author_attestation","sign_citation":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C/action/citation_signature","submit_replication":"https://pith.science/pith/H7WRB6W2TINJJCLLJU6BVXSV5C/action/replication_record"}},"created_at":"2026-07-05T08:11:02.453138+00:00","updated_at":"2026-07-05T08:11:02.453138+00:00"}