{"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/xflow-1d-2d-cross-modal-deep-neural-networks","title":"XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification","arxiv_id":"1709.00572","date":"2017-09-02","proceeding":null,"authors":["Cătălina Cangea","Petar Veličković","Pietro Liò"],"abstract":"In recent years, there have been numerous developments towards solving\nmultimodal tasks, aiming to learn a stronger representation than through a\nsingle modality. Certain aspects of the data can be particularly useful in this\ncase - for example, correlations in the space or time domain across modalities\n- but should be wisely exploited in order to benefit from their full predictive\npotential. We propose two deep learning architectures with multimodal\ncross-connections that allow for dataflow between several feature extractors\n(XFlow). Our models derive more interpretable features and achieve better\nperformances than models which do not exchange representations, usefully\nexploiting correlations between audio and visual data, which have a different\ndimensionality and are nontrivially exchangeable. Our work improves on existing\nmultimodal deep learning algorithms in two essential ways: (1) it presents a\nnovel method for performing cross-modality (before features are learned from\nindividual modalities) and (2) extends the previously proposed\ncross-connections which only transfer information between streams that process\ncompatible data. Illustrating some of the representations learned by the\nconnections, we analyse their contribution to the increase in discrimination\nability and reveal their compatibility with a lip-reading network intermediate\nrepresentation. We provide the research community with Digits, a new dataset\nconsisting of three data types extracted from videos of people saying the\ndigits 0-9. Results show that both cross-modal architectures outperform their\nbaselines (by up to 11.5%) when evaluated on the AVletters, CUAVE and Digits\ndatasets, achieving state-of-the-art results.","url_abs":"http://arxiv.org/abs/1709.00572v2","url_pdf":"http://arxiv.org/pdf/1709.00572v2.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":"xflow-1d-2d-cross-modal-deep-neural-networks","repo_url":"https://github.com/catalina17/XFlow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lip-reading","task_name":"Lip Reading"},{"task_slug":"multimodal-deep-learning","task_name":"Multimodal Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.00572","atlas_url":"https://app.syntology.ai/?focus=1709.00572","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}