{"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/crossmodal-clustered-contrastive-learning","title":"Crossmodal clustered contrastive learning: Grounding of spoken language to gesture","arxiv_id":null,"date":"2021-06-23","proceeding":"ACM ICMI Workshop GENEA 2021 10","authors":["Dong Won Lee","Chaitanya Ahuja","Louis-Philippe Morency"],"abstract":"Crossmodal grounding is a key challenge for the task of generating relevant and well-timed gestures from just spoken language as an input. Often, the same gesture can be accompanied by semantically different spoken language phrases which makes crossmodal grounding especially challenging. For example, a deictic gesture of spanning a region could co-occur with semantically different phrases \"entire bottom row\" (referring to a physical point) and \"molecules expand and decay\" (referring to a scientific phenomena). In this paper, we introduce a self-supervised approach to learn such many-to-one grounding relationships between spoken language and gestures. As part of this approach, we propose a new contrastive loss function, Crossmodal Cluster NCE , that guides the model to learn spoken language representations which are consistent with the similarities in the gesture space. By doing so, we impose a greater level of grounding between spoken language and gestures in the model. We demonstrate the effectiveness of our approach on a publicly available dataset through quantitative and qualitative studies. Our proposed methodology significantly outperforms prior approaches for grounding gestures to language. Link to code: https://github.com/dondongwon/CC_NCE_GENEA.","url_abs":"https://openreview.net/forum?id=o8CpxaBurZQ","url_pdf":"https://openreview.net/pdf?id=o8CpxaBurZQ","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":"crossmodal-clustered-contrastive-learning","repo_url":"https://github.com/dondongwon/cc_nce_genea","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}