πŸ“„
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention paper by Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan
⭐ 61.3
πŸ“„
Paper

EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention

by Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan 2305.07027
Free2AITools Nexus Index
61.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 91
P: Popularity 71
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named Efficient ViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, ...

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Semantic Scholar 600 Citations
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Registry ID 2305.07027
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2305_07027,
  author = {Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan},
  title = {EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2305.07027}},
  note = {Accessed via Free2AITools.}
}
APA Style
Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan. (2026). EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention [Paper]. Free2AITools. https://arxiv.org/abs/2305.07027

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Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 91
Popularity (P) 71
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention: Authority (A:91), Popularity (P:71), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named Efficient ViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, ..."

❝ Cite Node 

@article{Liu2026EfficientViT:,
  title={EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention},
  author={Xinyu Liu and Houwen Peng and Ningxin Zheng and Yuqing Yang and Han Hu and Yixuan Yuan},
  journal={arXiv preprint arXiv:2305.07027},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Xinyu Liu Houwen Peng Ningxin Zheng Yuqing Yang Han Hu Yixuan Yuan

πŸ”— Full Paper

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πŸ“Š Research Signals

πŸ“ˆ600CitationsSemantic Scholar
πŸ›οΈ91AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈvision multimediaField

🏷️ Research Topics

vision modelstransformer architecture
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πŸ†” Identity & Source

id
2305.07027
slug
2305.07027
source
semantic_scholar
author
Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

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citations
600

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