{"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-one-shot-learning-of-speech-and","title":"Multimodal One-Shot Learning of Speech and Images","arxiv_id":"1811.03875","date":"2018-11-09","proceeding":null,"authors":["Ryan Eloff","Herman A. Engelbrecht","Herman Kamper"],"abstract":"Imagine a robot is shown new concepts visually together with spoken tags,\ne.g. \"milk\", \"eggs\", \"butter\". After seeing one paired audio-visual example per\nclass, it is shown a new set of unseen instances of these objects, and asked to\npick the \"milk\". Without receiving any hard labels, could it learn to match the\nnew continuous speech input to the correct visual instance? Although unimodal\none-shot learning has been studied, where one labelled example in a single\nmodality is given per class, this example motivates multimodal one-shot\nlearning. Our main contribution is to formally define this task, and to propose\nseveral baseline and advanced models. We use a dataset of paired spoken and\nvisual digits to specifically investigate recent advances in Siamese\nconvolutional neural networks. Our best Siamese model achieves twice the\naccuracy of a nearest neighbour model using pixel-distance over images and\ndynamic time warping over speech in 11-way cross-modal matching.","url_abs":"http://arxiv.org/abs/1811.03875v2","url_pdf":"http://arxiv.org/pdf/1811.03875v2.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-one-shot-learning-of-speech-and","repo_url":"https://github.com/rpeloff/multimodal-one-shot-learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"multimodal-one-shot-learning-of-speech-and","repo_url":"https://github.com/rpeloff/multimodal_one_shot_learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.03875","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}