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Wenyu Liu

15 papers here

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

author position
  • first author2
  • middle author6
  • last author7

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CV14
  • stat.ML1
ORCID 0000-0002-4582-7488
same name
  • Wenyu Liu — 70 papers, h 40
  • Wenyu Liu — 30 papers, h 15
  • Wenyu Liu — 19 papers, h 7
  • Wenyu Liu — 17 papers, h 12
  • Wenyu Liu — 7 papers, h 2
  • Wenyu Liu — 5 papers, h 4

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
20142022
most citedRevisiting Multiple Instance Neural Networks

512 citations · 1.2k across the 15 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 11 cited

Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis

Pengyuan Lyu, Xiang Bai, Cong Yao +3

In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig.…

cs.CV2017★ 25 cited

Point Linking Network for Object Detection

Xinggang Wang, Kaibing Chen, Zilong Huang +2

Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets…

cs.CV2017

Deep Patch Learning for Weakly Supervised Object Classification and Discovery

Peng Tang, Xinggang Wang, Zilong Huang +2

Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.…

cs.CV2017★ 29 cited

Multiple Instance Detection Network with Online Instance Classifier Refinement

Peng Tang, Xinggang Wang, Xiang Bai +1

Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results…

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