title: Make Your Training Flexible: Towards Deployment-Efficient Video Models
summary: Popular video training methods mainly operate on a fixed number of tokens
  sampled from a predetermined spatiotemporal grid, resulting in sub-optimal
  accuracy-computation trade-offs due to inherent video redundancy. They also
  lack adaptability to varying computational budgets for downstream tasks,
  hindering applications of the most competitive model in real-world scenes. We
  thus propose a new test setting, Token Optimization, for maximized input
  information across budgets, which optimizes the size-limited set of input
  tokens through token selection from more suitably sampled videos. To this end,
  we propose a novel augmentation tool termed Flux. By making the sampling grid
  flexible and leveraging token selection, it is easily adopted in most popular
  video training frameworks, boosting model robustness with nearly no additional
  cost. We integrate Flux in large-scale video pre-training, and the resulting
  FluxViT establishes new state-of-the-art results across extensive tasks at
  standard costs. Notably, with 1/4 tokens only, it can still match the
  performance of previous state-of-the-art models with Token Optimization,
  yielding nearly 90\% savings. All models and data are available at
  https://github.com/OpenGVLab/FluxViT.
paper_authors._id: 67db7a33b1d42828a18fd0c8
paper_authors.name: Chenting Wang
paper_authors._id: 67db7a33b1d42828a18fd0c9
paper_authors.name: Kunchang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T10:27:30.569Z
paper_authors._id: 61fb81006374891646732f37
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1643872995181-61fb81006374891646732f37.jpeg
paper_authors.fullname: Kunchang Li
paper_authors.type: user
paper_authors.user: Andy1621
paper_authors._id: 67db7a33b1d42828a18fd0ca
paper_authors.name: Tianxiang Jiang
paper_authors._id: 67db7a33b1d42828a18fd0cb
paper_authors.name: Xiangyu Zeng
paper_authors._id: 67db7a33b1d42828a18fd0cc
paper_authors.name: Yi Wang
paper_authors._id: 67db7a33b1d42828a18fd0cd
paper_authors.name: Limin Wang
paper_title: Make Your Training Flexible: Towards Deployment-Efficient Video Models
paper_summary: Popular video training methods mainly operate on a fixed number of tokens
  sampled from a predetermined spatiotemporal grid, resulting in sub-optimal
  accuracy-computation trade-offs due to inherent video redundancy. They also
  lack adaptability to varying computational budgets for downstream tasks,
  hindering applications of the most competitive model in real-world scenes. We
  thus propose a new test setting, Token Optimization, for maximized input
  information across budgets, which optimizes the size-limited set of input
  tokens through token selection from more suitably sampled videos. To this end,
  we propose a novel augmentation tool termed Flux. By making the sampling grid
  flexible and leveraging token selection, it is easily adopted in most popular
  video training frameworks, boosting model robustness with nearly no additional
  cost. We integrate Flux in large-scale video pre-training, and the resulting
  FluxViT establishes new state-of-the-art results across extensive tasks at
  standard costs. Notably, with 1/4 tokens only, it can still match the
  performance of previous state-of-the-art models with Token Optimization,
  yielding nearly 90\% savings. All models and data are available at
  https://github.com/OpenGVLab/FluxViT.
paper_ai_summary: A novel token optimization technique, Flux, improves video training by selecting more suitable tokens, enhancing model performance with reduced computational cost.
paper_ai_keywords: token optimization token selection video training Flux sampling grid model robustness large-scale video pre-training FluxViT