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Chang Xu

69 papers hereh-index 5112.9k citations611 works total

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

author position
  • sole author1
  • first author11
  • middle author39
  • last author17

Across the 68 of 69 papers where every author was matched, so the position is known.

fields
  • nucl-th29
  • cs.CV16
  • math.PR6
  • cs.CR3
  • cs.SE3
  • cond-mat.soft2
same name
  • Chang Xu — 52 papers
  • Chang Xu — 24 papers, h 13
  • Chang Xu — 22 papers, h 33
  • Chang Xu — 20 papers, h 7
  • Chang Xu — 19 papers, h 6
  • Chang Xu — 13 papers, h 43

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
20062023
most citedGhostNetV2: Enhance Cheap Operation with Long-Range Attention

278 citations · 1.7k across the 69 of their papers we have counts for

collaborators
Showing 2021 · cs.CVShow all

4 papers · 2 filters

cs.CV2021★ 5 cited

An Image Patch is a Wave: Phase-Aware Vision MLP

Yehui Tang, Kai Han, Jianyuan Guo +4

In the field of computer vision, recent works show that a pure MLP architecture mainly stacked by fully-connected layers can achieve competing performance with CNN and transformer.…

cs.CV2021★ 8 cited

Learning Versatile Convolution Filters for Efficient Visual Recognition

Kai Han, Yunhe Wang, Chang Xu +3

This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of ef…

cs.CV2021★ 5 cited

DeepFake MNIST+: A DeepFake Facial Animation Dataset

Jiajun Huang, Xueyu Wang, Bo Du +2

The DeepFakes, which are the facial manipulation techniques, is the emerging threat to digital society. Various DeepFake detection methods and datasets are proposed for detecting s…

cs.CV2021★ 10 cited

Neural Architecture Dilation for Adversarial Robustness

Yanxi Li, Zhaohui Yang, Yunhe Wang +1

With the tremendous advances in the architecture and scale of convolutional neural networks (CNNs) over the past few decades, they can easily reach or even exceed the performance o…

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