  In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified
architecture for image and video classification, as well as object detection.
We present an improved version of MViT that incorporates decomposed relative
positional embeddings and residual pooling connections. We instantiate this
architecture in five sizes and evaluate it for ImageNet classification, COCO
detection and Kinetics video recognition where it outperforms prior work. We
further compare MViTv2s' pooling attention to window attention mechanisms where
it outperforms the latter in accuracy/compute. Without bells-and-whistles,
MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on
ImageNet classification, 58.7 boxAP on COCO object detection as well as 86.1%
on Kinetics-400 video classification. Code and models are available at
https://github.com/facebookresearch/mvit.
