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Zhaofeng He

4 papers hereh-index 211.8k citations48 works total

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

author position
  • middle author1
  • last author3

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

fields
  • cs.CV3
  • cs.LG1
same name
  • Zhaofeng He — 17 papers, h 4
  • Zhaofeng He — 8 papers, h 3
  • Zhaofeng He — 8 papers, h 6
  • Zhaofeng He — 6 papers, h 7
  • Zhaofeng He — 5 papers, h 3
  • Zhaofeng He — 3 papers, h 2

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 citedLearning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

4 citations · 5 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2023★ 4 cited

Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

Rui Wang, Peipei Li, Huaibo Huang +3

We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them pro…

cs.CV2023

CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue

Xing Cui, Zekun Li, Peipei Li +3

This paper explores interactive facial image editing via dialogue and introduces the ChatEdit benchmark dataset for evaluating image editing and conversation abilities in this cont…

cs.CV2023★ 1 cited

Pluralistic Aging Diffusion Autoencoder

Peipei Li, Rui Wang, Huaibo Huang +2

Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. Most existing methods often produce one deterministic estimation. This…

cs.LG2023

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective

Baoyuan Wu, Zihao Zhu, Li Liu +3

Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have be…

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