{"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/a-survey-on-interpretable-cross-modal","title":"A Survey on Interpretable Cross-modal Reasoning","arxiv_id":"2309.01955","date":"2023-09-05","proceeding":null,"authors":["Dizhan Xue","Shengsheng Qian","Zuyi Zhou","Changsheng Xu"],"abstract":"In recent years, cross-modal reasoning (CMR), the process of understanding and reasoning across different modalities, has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. As the deployment of AI systems becomes more ubiquitous, the demand for transparency and comprehensibility in these systems' decision-making processes has intensified. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR), where the objective is not only to achieve high predictive performance but also to provide human-understandable explanations for the results. This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR. Furthermore, this survey reviews the existing CMR datasets with annotations for explanations. Finally, this survey summarizes the challenges for I-CMR and discusses potential future directions. In conclusion, this survey aims to catalyze the progress of this emerging research area by providing researchers with a panoramic and comprehensive perspective, illuminating the state of the art and discerning the opportunities. The summarized methods, datasets, and other resources are available at https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning.","url_abs":"https://arxiv.org/abs/2309.01955v2","url_pdf":"https://arxiv.org/pdf/2309.01955v2.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":"a-survey-on-interpretable-cross-modal","repo_url":"https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"explanation-generation","task_name":"Explanation Generation"},{"task_slug":"factual-visual-question-answering","task_name":"Factual Visual Question Answering"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"image-guided-story-ending-generation","task_name":"Image-guided Story Ending Generation"},{"task_slug":"phrase-grounding","task_name":"Phrase Grounding"},{"task_slug":"science-question-answering","task_name":"Science Question Answering"},{"task_slug":"survey","task_name":"Survey"},{"task_slug":"visual-commonsense-reasoning","task_name":"Visual Commonsense Reasoning"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.01955","atlas_url":"https://app.syntology.ai/?focus=2309.01955","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}