  Vision Transformers (ViT) have emerged as the de-facto choice for numerous
industry grade vision solutions. But their inference cost can be prohibitive
for many settings, as they compute self-attention in each layer which suffers
from quadratic computational complexity in the number of tokens. On the other
hand, spatial information in images and spatio-temporal information in videos
is usually sparse and redundant. In this work, we introduce LookupViT, that
aims to exploit this information sparsity to reduce ViT inference cost.
LookupViT provides a novel general purpose vision transformer block that
operates by compressing information from higher resolution tokens to a fixed
number of tokens. These few compressed tokens undergo meticulous processing,
while the higher-resolution tokens are passed through computationally cheaper
layers. Information sharing between these two token sets is enabled through a
bidirectional cross-attention mechanism. The approach offers multiple
advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via
standard high-level operators, (b) applicable to standard ViT and its variants,
thus generalizes to various tasks, (c) can handle different tokenization and
attention approaches. LookupViT also offers flexibility for the compressed
tokens, enabling performance-computation trade-offs in a single trained model.
We show LookupViT's effectiveness on multiple domains - (a) for
image-classification (ImageNet-1K and ImageNet-21K), (b) video classification
(Kinetics400 and Something-Something V2), (c) image captioning (COCO-Captions)
with a frozen encoder. LookupViT provides $2\times$ reduction in FLOPs while
upholding or improving accuracy across these domains. In addition, LookupViT
also demonstrates out-of-the-box robustness and generalization on image
classification (ImageNet-C,R,A,O), improving by up to $4\%$ over ViT.
