{"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/multimodal-prompting-with-missing-modalities","title":"Multimodal Prompting with Missing Modalities for Visual Recognition","arxiv_id":"2303.03369","date":"2023-03-06","proceeding":"CVPR 2023 1","authors":["Yi-Lun Lee","Yi-Hsuan Tsai","Wei-Chen Chiu","Chen-Yu Lee"],"abstract":"In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model re-training. Code is available.","url_abs":"https://arxiv.org/abs/2303.03369v2","url_pdf":"https://arxiv.org/pdf/2303.03369v2.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":"multimodal-prompting-with-missing-modalities","repo_url":"https://github.com/yilunlee/missing_aware_prompts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"multimodal-prompting-with-missing-modalities","repo_url":"https://github.com/hulianyuyy/deep_correlated_prompting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prompt-learning","task_name":"Prompt Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.03369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.03369"}},"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/hulianyuyy/deep_correlated_prompting","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yilunlee/missing_aware_prompts","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/YiLunLee/missing","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1},"listed":{"samples":1,"ran":1,"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":2,"samples":[{"code_sha256_prefix":"32054cdf099cddf1","entry":"MultiModalPromptLearner","repo":"hulianyuyy/deep_correlated_prompting","repo_kind":"listed","path":"clip/modules/clip_missing_aware_prompt_module.py","file_url":"https://github.com/hulianyuyy/deep_correlated_prompting/blob/HEAD/clip/modules/clip_missing_aware_prompt_module.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"32054cdf099cddf1"}},{"code_sha256_prefix":"339c88df9abe5928","entry":"ViLTransformerSS","repo":"yilunlee/missing_aware_prompts","repo_kind":"official","path":"vilt/modules/vilt_missing_aware_prompt_module.py","file_url":"https://github.com/yilunlee/missing_aware_prompts/blob/HEAD/vilt/modules/vilt_missing_aware_prompt_module.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"339c88df9abe5928"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}