Turbo training with token dropout
The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, vid...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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British Machine Vision Association
2022
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| _version_ | 1797108452558897152 |
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| author | Han, T Xie, W Zisserman, A |
| author_facet | Han, T Xie, W Zisserman, A |
| author_sort | Han, T |
| collection | OXFORD |
| description | The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity classification, showing that Turbo training can largely maintain competitive performance while achieving almost 4× speed-up and significantly less memory consumption. (3) Turbo training enables long-schedule video-language training and end-to-end long-video training, delivering competitive or superior performance than previous works, which were infeasible to train under limited resources. |
| first_indexed | 2024-03-07T07:27:58Z |
| format | Conference item |
| id | oxford-uuid:d1e1a0f7-9ecc-4617-bf0a-1abb56aafdb1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:27:58Z |
| publishDate | 2022 |
| publisher | British Machine Vision Association |
| record_format | dspace |
| spelling | oxford-uuid:d1e1a0f7-9ecc-4617-bf0a-1abb56aafdb12022-12-02T10:45:01ZTurbo training with token dropoutConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d1e1a0f7-9ecc-4617-bf0a-1abb56aafdb1EnglishSymplectic ElementsBritish Machine Vision Association2022Han, TXie, WZisserman, AThe objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity classification, showing that Turbo training can largely maintain competitive performance while achieving almost 4× speed-up and significantly less memory consumption. (3) Turbo training enables long-schedule video-language training and end-to-end long-video training, delivering competitive or superior performance than previous works, which were infeasible to train under limited resources. |
| spellingShingle | Han, T Xie, W Zisserman, A Turbo training with token dropout |
| title | Turbo training with token dropout |
| title_full | Turbo training with token dropout |
| title_fullStr | Turbo training with token dropout |
| title_full_unstemmed | Turbo training with token dropout |
| title_short | Turbo training with token dropout |
| title_sort | turbo training with token dropout |
| work_keys_str_mv | AT hant turbotrainingwithtokendropout AT xiew turbotrainingwithtokendropout AT zissermana turbotrainingwithtokendropout |