mlsanityData to 14 Oct 2026

MMViT: Multiscale Multiview Vision Transformers

arXiv 2305.00104 ↗First posted 28 Apr 2023cs.CV eess.AS eess.IVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 31 references that are in the map. First posted on arXiv 28 Apr 2023.

Builds on

27 of 31 references.

References · 27

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15 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023ODEFormer: Symbolic Regression of Dynamical Systems with Transformerscited by 62
  2. 2024From Coarse to Fine: Efficient Training for Audio Spectrogram Transformerscited by 1
  3. 2026Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorderscited by 0