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Jun Zhou

12 papers hereh-index 10666 citations14 works total

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

author position
  • first author1
  • middle author9
  • last author2

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.LG2
  • cs.CL1
  • cs.IR1
same name
  • Jun Zhou — 33 papers, h 26
  • Jun Zhou — 28 papers, h 20
  • Jun Zhou — 23 papers, h 22
  • Jun Zhou — 22 papers, h 10
  • Jun Zhou — 21 papers, h 20
  • Jun Zhou — 17 papers, h 7

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 citedPP-OCR: A Practical Ultra Lightweight OCR System

111 citations · 196 across the 12 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020

STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting

Mingjie Wang, Hao Cai, Xianfeng Han +2

Crowd counting remains a challenging task because the presence of drastic scale variation, density inconsistency, and complex background can seriously degrade the counting accuracy…

cs.CV2020★ 111 cited

PP-OCR: A Practical Ultra Lightweight OCR System

Yuning Du, Chenxia Li, Ruoyu Guo +8

The Optical Character Recognition (OCR) systems have been widely used in various of application scenarios, such as office automation (OA) systems, factory automations, online educa…

cs.IR2020★ 1 cited

RNE: A Scalable Network Embedding for Billion-scale Recommendation

Jianbin Lin, Daixin Wang, Lu Guan +5

Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dy…

cs.CL2020★ 13 cited

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

Yankun Ren, Jianbin Lin, Siliang Tang +4

Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods ge…

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