Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Chaitanya Ryali Yuan-Ting Hu Daniel Bolya Chen Wei Haoqi Fan Po-Yao Huang Vaibhav Aggarwal Arkabandhu Chowdhury Omid Poursaeed Judy Hoffman Jitendra Malik Yanghao Li Christoph Feichtenhofer
Modern hierarchical vision transformers have added several vision-specific
components in the pursuit of supervised classification performance. While these
components lead to effective accuracies and attractive FLOP counts, the added
complexity actually makes these transformers slower than their vanilla ViT
counterparts. In this paper, we argue that this additional bulk is unnecessary.
By pretraining with a strong visual pretext task (MAE), we can strip out all
the bells-and-whistles from a state-of-the-art multi-stage vision transformer
without losing accuracy. In the process, we create Hiera, an extremely simple
hierarchical vision transformer that is more accurate than previous models
while being significantly faster both at inference and during training. We
evaluate Hiera on a variety of tasks for image and video recognition. Our code
and models are available at https://github.com/facebookresearch/hiera.