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Guangtao Wang

4 papers here

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.AI1
  • cs.LG1
ORCID 0000-0003-2766-0917
same name
  • Guangtao Wang — 9 papers, h 27
  • Guangtao Wang — 1 paper
  • Guangtao Wang — 1 paper, h 23
  • Guangtao Wang — 1 paper

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

most citedImbalanced Node Classification Beyond Homophilic Assumption

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023

EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection

Guangtao Wang, Jun Li, Jie Xie +2

In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited v…

cs.AI2023★ 1 cited

Imbalanced Node Classification Beyond Homophilic Assumption

Jie Liu, Mengting He, Guangtao Wang +3

Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe perfor…

cs.CV2023

EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection

Guangtao Wang, Jun Li, Zhijian Wu +3

In recent years, deep convolutional neural networks (CNN) have significantly advanced face detection. In particular, lightweight CNNbased architectures have achieved great success…

cs.LG2022

SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences

Peng Qi, Guangtao Wang, Jing Huang

Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute…

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