# MMViT: Multiscale Multiview Vision Transformers

Source: [MMViT: Multiscale Multiview Vision Transformers](<https://www.sota2.com/research/paper/68fa6f341e209131cea446ea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:15:37.285Z

Authors: Yuchen Liu, Natasha Ong, Kaiyan Peng, Bo Xiong, Qifan Wang, Rui Hou, Madian Khabsa, Kaiyue Yang, David Liu, Donald S. Williamson, Hanchao Yu

Published: 2023-04-28T21:51:41.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.00104>)

[PDF](<https://arxiv.org/pdf/2305.00104.pdf>)