In this paper, we present a new approach for model acceleration by exploiting
spatial sparsity in visual data. We observe that the final prediction in vision
Transformers is only based on a subset of the most informative tokens, which is
sufficient for accurate image recognition. Based on this observation, we
propose a dynamic token sparsification framework to prune redundant tokens
progressively and dynamically based on the input to accelerate vision
Transformers. Specifically, we devise a lightweight prediction module to
estimate the importance score of each token given the current features. The
module is added to different layers to prune redundant tokens hierarchically.
While the framework is inspired by our observation of the sparse attention in
vision Transformers, we find the idea of adaptive and asymmetric computation
can be a general solution for accelerating various architectures. We extend our
method to hierarchical models including CNNs and hierarchical vision
Transformers as well as more complex dense prediction tasks that require
structured feature maps by formulating a more generic dynamic spatial
sparsification framework with progressive sparsification and asymmetric
computation for different spatial locations. By applying lightweight fast paths
to less informative features and using more expressive slow paths to more
important locations, we can maintain the structure of feature maps while
significantly reducing the overall computations. Extensive experiments
demonstrate the effectiveness of our framework on various modern architectures
and different visual recognition tasks. Our results clearly demonstrate that
dynamic spatial sparsification offers a new and more effective dimension for
model acceleration. Code is available at
this https URL

本文提出了一种通过利用视觉数据中的空间稀疏性进行模型加速的新方法，该方法基于所提出的动态令牌稀疏化框架，并通过自适应和不对称计算等方式推广到各种体系结构中，通过对不重要的特征使用轻量级快速路径和对更重要位置使用更具表现力的慢速路径，可以显著减少总体计算量，实验结果表明动态空间稀疏化为模型加速提供了新的更有效的解决方案。