Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks

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 https://github.com/raoyongming/DynamicViTComment: Accepted to T-PAMI. Journal version of our NeurIPS 2021 work: arXiv:2106.02034. Code is available at https://github.com/raoyongming/DynamicVi

Fermi Surface and Band Renormalization in (Sr,K)Fe2_2As2_2 Superconductor from Angle-Resolved Photoemission Spectroscopy

High resolution angle-resolved photoemission measurements have been carried out on (Sr,K)Fe$_2$As$_2$ superconductor (Tc=21 K). Three hole-like Fermi surface sheets are clearly resolved for the first time around the Gamma point. The overall electronic structure shows significant difference from the band structure calculations. Qualitative agreement between the measured and calculated band structure is realized by assuming a chemical potential shift of -0.2 eV. The obvious band renormalization suggests the importance of electron correlation in understanding the electronic structure of the Fe-based compounds.Comment: 4 pages, 4 figure