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researcher

Yu Kong

4 papers hereh-index 5185 citations5 works total

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

author position
  • first author1
  • middle author2
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Yu Kong — 11 papers, h 5
  • Yu Kong — 10 papers, h 10
  • Yu Kong — 6 papers, h 2
  • Yu Kong — 5 papers, h 25
  • Yu Kong — 4 papers, h 1
  • Yu Kong — 2 papers, h 2

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
20192023
most citedLook Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator

5 citations · 6 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 1 cited

Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder

Xinmiao Lin, Yikang Li, Jenhao Hsiao +2

The popular VQ-VAE models reconstruct images through learning a discrete codebook but suffer from a significant issue in the rapid quality degradation of image reconstruction as th…

cs.CV2022★ 5 cited

Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator

Yuxin Kong, Canjie Luo, Weihong Ma +4

Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the st…

cs.CV2019

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

Zhichao Fu, Yu Kong, Yingbin Zheng +4

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the…

cs.CV2019

Edge-Aware Deep Image Deblurring

Zhichao Fu, Tianlong Ma, Yingbin Zheng +3

Image deblurring is a fundamental and challenging low-level vision problem. Previous vision research indicates that edge structure in natural scenes is one of the most important fa…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.