  We present Multiscale Multiview Vision Transformers (MMViT), which introduces
multiscale feature maps and multiview encodings to transformer models. Our
model encodes different views of the input signal and builds several
channel-resolution feature stages to process the multiple views of the input at
different resolutions in parallel. At each scale stage, we use a
cross-attention block to fuse information across different views. This enables
the MMViT model to acquire complex high-dimensional representations of the
input at different resolutions. The proposed model can serve as a backbone
model in multiple domains. We demonstrate the effectiveness of MMViT on audio
and image classification tasks, achieving state-of-the-art results.
