核心概念
Efficiently train high-resolution vision transformers using a multi-window strategy for improved performance and reduced training costs.
摘要
The content discusses a novel strategy, Win-Win, for training high-resolution vision transformers efficiently. It introduces the concept of masking most input tokens during training to reduce complexity and memory usage. The approach allows for direct processing of high-resolution inputs at test time without additional tricks. The strategy is applied to tasks like semantic segmentation, monocular depth prediction, and optical flow estimation, showing promising results in terms of performance and efficiency.
Directory:
- Abstract
- Introduction
- Data Extraction Methodologies
- Experiments on Monocular Tasks
- Experiments on Binocular Tasks
- Conclusion
Abstract:
- Transformers are widely used in vision architectures but face challenges with high-resolution tasks.
- Win-Win strategy masks most input tokens during training for efficient processing at test time.
- Results show improved performance and reduced training costs across various dense prediction tasks.
Introduction:
- Discusses challenges with global self-attention in ViTs for high-resolution images.
- Introduces the Win-Win strategy as a novel approach to efficient training and inference.
Data Extraction Methodologies:
- "It is 4 times faster to train than a full-resolution network."
- "Win-Win allows reducing the training time by a factor 3∼4 while reaching similar performance."
Experiments on Monocular Tasks:
- Evaluates different window configurations for optimal performance.
- Compares Win-Win strategy with other baselines, showcasing superior results.
Experiments on Binocular Tasks:
- Explores multi-window training strategies for optical flow estimation.
- Reports results on MPI-Sintel validation set comparing Win-Win with other methods.
Conclusion:
- Highlights the effectiveness of the Win-Win strategy in efficiently training high-resolution vision transformers.
統計資料
"It is 4 times faster to train than a full-resolution network."
"Win-Win allows reducing the training time by a factor 3∼4 while reaching similar performance."