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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions paper by Independent / Community
⭐ 64.4
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Paper

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

by Independent / Community 2102.12122
Free2AITools Nexus Index
64.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 95
P: Popularity 77
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to curren...

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Semantic Scholar 4.6K Citations
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Registry ID 2102.12122
License ArXiv
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2102_12122,
  author = {Unknown},
  title = {Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2102.12122}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions [Paper]. Free2AITools. https://arxiv.org/abs/2102.12122

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 95
Popularity (P) 77
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions: Authority (A:95), Popularity (P:77), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to curren..."

❝ Cite Node 

@article{Wang2026Pyramid,
  title={Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions},
  author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and Tong Lu and P. Luo and Ling Shao},
  journal={arXiv preprint arXiv:2102.12122},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu P. Luo Ling Shao

πŸ”— Full Paper

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πŸ“Š Research Signals

πŸ“ˆ4,635CitationsSemantic Scholar
πŸ›οΈ95AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈvision multimediaField

🏷️ Research Topics

image generationtransformer architecturevision models
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πŸ†” Identity & Source

id
2102.12122
slug
2102.12122
source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

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citations
4,635

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