{"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/large-scale-video-classification-with-1","title":"Large-Scale Video Classification with Convolutional Neural Networks","arxiv_id":null,"date":"2014-06-23","proceeding":"2014 IEEE Conference on Computer Vision and Pattern Recognition 2014 6","authors":["Andrej Karpathy","George Toderici","Sanketh Shetty","Thomas Leung","Rahul Sukthankar","Li Fei-Fei"],"abstract":"Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).","url_abs":"https://doi.org/10.1109/CVPR.2014.223","url_pdf":"https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42455.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":"large-scale-video-classification-with-1","repo_url":"https://github.com/lRomul/ball-action-spotting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"},{"task_slug":"video-classification","task_name":"Video Classification"}],"methods":[],"datasets_introduced":[{"slug":"sports-1m","name":"Sports-1M","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-recognition-in-videos-on-sports-1m","task":"Action Recognition","dataset":"Sports-1M","model":"DeepVideo’s Slow Fusion","rank_in_archive_order":9,"of":9,"metrics":{"Clip Hit@1":"41.9","Video hit@1 ":"60.9","Video hit@5":"80.2"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-ucf101","task":"Action Recognition","dataset":"UCF101","model":"Slow Fusion + Finetune top 3 layers","rank_in_archive_order":85,"of":91,"metrics":{"3-fold Accuracy":"65.4"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}