{"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/dynamic-modality-interaction-modeling-for","title":"Dynamic Modality Interaction Modeling for Image-Text Retrieval","arxiv_id":null,"date":"2021-07-11","proceeding":"ACM Special Interest Group on Information Retrieval 2021 7","authors":["Leigang Qu","Meng Liu","Jianlong Wu","Zan Gao","Liqiang Nie"],"abstract":"Image-text retrieval is a fundamental and crucial branch in information retrieval. Although much progress has been made in bridging vision and language, it remains challenging because of the difficult intra-modal reasoning and cross-modal alignment. Existing modality interaction methods have achieved impressive results on public datasets. However, they heavily rely on expert experience and empirical feedback towards the design of interaction patterns, therefore, lacking flexibility. To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To endow the model with the capability of path decision, we integrate a dynamic router in each cell for pattern exploration. As the routers are conditioned on inputs, our model can dynamically learn different activated paths for different data. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, verify the superiority of our model compared with several state-of-the-art baselines.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3404835.3462829#d65226017e1","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3404835.3462829","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":"dynamic-modality-interaction-modeling-for","repo_url":"https://github.com/LgQu/DIME","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"image-text-retrieval","task_name":"Image-text Retrieval"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-retrieval","task_name":"Text Retrieval"},{"task_slug":"cross-modal-alignment","task_name":"cross-modal alignment"},{"task_slug":"multimodal-interaction","task_name":"multimodal interaction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}