{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/uniadapter-unified-parameter-efficient","title":"UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling","arxiv_id":"2302.06605","date":"2023-02-13","proceeding":null,"authors":["Haoyu Lu","Yuqi Huo","Guoxing Yang","Zhiwu Lu","Wei Zhan","Masayoshi Tomizuka","Mingyu Ding"],"abstract":"Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the standard full fine-tuning paradigm becomes unsustainable due to heavy computational and storage costs. This paper proposes UniAdapter, which unifies unimodal and multimodal adapters for parameter-efficient cross-modal adaptation on pre-trained vision-language models. Specifically, adapters are distributed to different modalities and their interactions, with the total number of tunable parameters reduced by partial weight sharing. The unified and knowledge-sharing design enables powerful cross-modal representations that can benefit various downstream tasks, requiring only 1.0%-2.0% tunable parameters of the pre-trained model. Extensive experiments on 6 cross-modal downstream benchmarks (including video-text retrieval, image-text retrieval, VideoQA, and VQA) show that in most cases, UniAdapter not only outperforms the state-of-the-arts, but even beats the full fine-tuning strategy. Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49.7% recall@1 with 2.2% model parameters, outperforming the latest competitors by 2.0%. The code and models are available at https://github.com/RERV/UniAdapter.","url_abs":"https://arxiv.org/abs/2302.06605v2","url_pdf":"https://arxiv.org/pdf/2302.06605v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"uniadapter-unified-parameter-efficient","repo_url":"https://github.com/rerv/uniadapter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"uniadapter-unified-parameter-efficient","repo_url":"https://github.com/uniadapter/uniadapter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-text-retrieval","task_name":"Image-text Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-retrieval","task_name":"Text Retrieval"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"video-text-retrieval","task_name":"Video-Text Retrieval"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.06605","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06605"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rerv/uniadapter","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/uniadapter/uniadapter","reach":{"status":"ok"}}],"summary":{"ran_fixture":4,"ran_draft_wrong":2,"ran_violates":1,"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":10,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":10,"samples":[{"code_sha256_prefix":"78ca1debd9c4ed87","entry":"autocontrast_func","repo":"rerv/uniadapter","repo_kind":"official","path":"transform/randaugment.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/transform/randaugment.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"DEP_MISSING","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"78ca1debd9c4ed87"}},{"code_sha256_prefix":"aa81d03f07735ab8","entry":"compute_acc","repo":"rerv/uniadapter","repo_kind":"official","path":"utils.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"code_sha256_prefix":"ad12a494674d23fb","entry":"compute_n_params","repo":"rerv/uniadapter","repo_kind":"official","path":"utils.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"ad12a494674d23fb"}},{"code_sha256_prefix":"4c53169f63a1fcef","entry":"equalize_func","repo":"rerv/uniadapter","repo_kind":"official","path":"transform/randaugment.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/transform/randaugment.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"DEP_MISSING","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"4c53169f63a1fcef"}},{"code_sha256_prefix":"1dd2ab205689d45b","entry":"identity_func","repo":"rerv/uniadapter","repo_kind":"official","path":"transform/randaugment.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/transform/randaugment.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"1dd2ab205689d45b"}},{"code_sha256_prefix":"c6ec173f19f5c34d","entry":"interpolate_pos_embed","repo":"rerv/uniadapter","repo_kind":"official","path":"models/vit.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/models/vit.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"c6ec173f19f5c34d"}},{"code_sha256_prefix":"c7f85279d772ea19","entry":"itm_eval","repo":"rerv/uniadapter","repo_kind":"official","path":"train_retrieval.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/train_retrieval.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"c7f85279d772ea19"}},{"code_sha256_prefix":"aa5a91226269bf75","entry":"tile","repo":"rerv/uniadapter","repo_kind":"official","path":"models/blip_vqa.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/models/blip_vqa.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"aa5a91226269bf75"}},{"code_sha256_prefix":"0ec9fc2025c16f65","entry":"all_gather_with_grad","repo":"rerv/uniadapter","repo_kind":"official","path":"models/blip_retrieval.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/models/blip_retrieval.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"0ec9fc2025c16f65"}},{"code_sha256_prefix":"73cecca9f3575f09","entry":"concat_all_gather","repo":"rerv/uniadapter","repo_kind":"official","path":"models/blip_retrieval.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/models/blip_retrieval.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":false,"mcp_get_code":{"code_sha256":"73cecca9f3575f09"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}