{"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/trecvit-a-recurrent-video-transformer","title":"TRecViT: A Recurrent Video Transformer","arxiv_id":"2412.14294","date":"2024-12-18","proceeding":null,"authors":["Viorica Pătrăucean","Xu Owen He","Joseph Heyward","Chuhan Zhang","Mehdi S. M. Sajjadi","George-Cristian Muraru","Artem Zholus","Mahdi Karami","Ross Goroshin","Yutian Chen","Simon Osindero","João Carreira","Razvan Pascanu"],"abstract":"We propose a novel block for video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (LRUs) perform information mixing over time, self-attention layers perform mixing over space, and MLPs over channels. The resulting architecture TRecViT performs well on sparse and dense tasks, trained in supervised or self-supervised regimes. Notably, our model is causal and outperforms or is on par with a pure attention model ViViT-L on large scale video datasets (SSv2, Kinetics400), while having $3\\times$ less parameters, $12\\times$ smaller memory footprint, and $5\\times$ lower FLOPs count. Code and checkpoints will be made available online at https://github.com/google-deepmind/trecvit.","url_abs":"https://arxiv.org/abs/2412.14294v1","url_pdf":"https://arxiv.org/pdf/2412.14294v1.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":"trecvit-a-recurrent-video-transformer","repo_url":"https://github.com/google-deepmind/trecvit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"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}