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researcher

Tao Han

Northwestern Polytechnical University

15 papers hereh-index 191.3k citations34 works total

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

author position
  • first author4
  • middle author7
  • last author1

Across the 12 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.AI1
  • cs.LG1
  • cs.NI1
  • cs.SE1
  • hep-ex1
affiliations
  • Northwestern Polytechnical University
same name
  • Tao Han — 20 papers, h 11
  • Tao Han — 15 papers, h 9
  • Tao Han — 11 papers, h 6
  • Tao Han — 10 papers, h 2
  • Tao Han — 10 papers, h 8
  • Tao Han — 8 papers, h 10

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
20122023
most citedNeuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

77 citations · 215 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 33 cited

Learning Independent Instance Maps for Crowd Localization

Junyu Gao, Tao Han, Qi Wang +2

Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse predic…

cs.CV2020★ 21 cited

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Tao Han, Junyu Gao, Yuan Yuan +1

Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learn…

cs.CV2020★ 77 cited

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

Qi Wang, Tao Han, Junyu Gao +1

Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain.…

cs.CV2020★ 4 cited

Focus on Semantic Consistency for Cross-domain Crowd Understanding

Tao Han, Junyu Gao, Yuan Yuan +1

For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models w…

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