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Yulun Zhang

ETH Zurich

46 papers hereh-index 4521.1k citations92 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author36
  • last author4

Across the 45 of 46 papers where every author was matched, so the position is known.

fields
  • cs.CV43
  • eess.IV3
affiliations
  • ETH Zurich
Homepage
same name
  • Yulun Zhang — 37 papers, h 11
  • Yulun Zhang — 30 papers, h 33
  • Yulun Zhang — 25 papers, h 8
  • Yulun Zhang — 20 papers, h 9
  • Yulun Zhang — 16 papers, h 5
  • Yulun Zhang — 14 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182023
most citedImage Super-Resolution Using Very Deep Residual Channel Attention Networks

289 citations · 1.5k across the 44 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 2 cited

Joint Super-Resolution and Alignment of Tiny Faces

Yu Yin, Joseph P. Robinson, Yulun Zhang +1

Super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. On the one hand, landmark localization could obtain higher accuracy with faces of high-re…

cs.CV2019★ 22 cited

Visual Semantic Reasoning for Image-Text Matching

Kunpeng Li, Yulun Zhang, Kai Li +2

Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global se…

cs.CV2019★ 9 cited

Multimodal Style Transfer via Graph Cuts

Yulun Zhang, Chen Fang, Yilin Wang +4

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alterna…

cs.CV2019★ 203 cited

Residual Non-local Attention Networks for Image Restoration

Yulun Zhang, Kunpeng Li, Kai Li +2

In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted ima…

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