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Bo Zhang

Xiaomi;Meituan

23 papers hereh-index 192.9k citations28 works total

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

author position
  • first author1
  • middle author15
  • last author3

Across the 19 of 23 papers where every author was matched, so the position is known.

fields
  • cs.CV14
  • cs.LG7
  • cs.CL1
  • eess.AS1
affiliations
  • Xiaomi;Meituan
same name
  • Bo Zhang — 51 papers, h 28
  • Bo Zhang — 36 papers, h 52
  • Bo Zhang — 29 papers, h 12
  • Bo Zhang — 28 papers, h 27
  • Bo Zhang — 22 papers
  • Bo Zhang — 22 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
20192024
most citedConditional Positional Encodings for Vision Transformers

411 citations · 887 across the 22 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 4 cited

MoGA: Searching Beyond MobileNetV3

Xiangxiang Chu, Bo Zhang, Ruijun Xu

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its sup…

cs.LG2019★ 72 cited

FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search

Xiangxiang Chu, Bo Zhang, Ruijun Xu

One of the most critical problems in weight-sharing neural architecture search is the evaluation of candidate models within a predefined search space. In practice, a one-shot super…

cs.CV2019★ 4 cited

A Matrix-in-matrix Neural Network for Image Super Resolution

Hailong Ma, Xiangxiang Chu, Bo Zhang +1

In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper lay…

cs.CV2019★ 86 cited

Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

Xiangxiang Chu, Bo Zhang, Hailong Ma +2

Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by usi…

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