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Lei Fan

26 papers hereh-index 8155 citations40 works total

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

author position
  • middle author15
  • last author10

Across the 25 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CV22
  • cs.CL3
  • cs.LG1
same name
  • Lei Fan — 11 papers, h 6
  • Lei Fan — 7 papers, h 10
  • Lei Fan — 6 papers, h 3
  • Lei Fan — 5 papers, h 8
  • Lei Fan — 4 papers, h 4
  • Lei Fan — 4 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

activity
20242026
most citedMultimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

1 citations · 2 across the 25 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics

Mingcheng Qu, Yuncong Wu, Donglin Di +4

Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability…

cs.CV2024

DH-FaceVid-1K: A Large-Scale High-Quality Dataset for Face Video Generation

Donglin Di, He Feng, Wenzhang Sun +6

Human-centric generative models are becoming increasingly popular, giving rise to various innovative tools and applications, such as talking face videos conditioned on text or audi…

cs.CV2024

GRPose: Learning Graph Relations for Human Image Generation with Pose Priors

Xiangchen Yin, Donglin Di, Lei Fan +6

Recent methods using diffusion models have made significant progress in human image generation with various control signals such as pose priors. However, existing efforts are still…

cs.CV2024

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection

Yun Liang, Zhiguang Hu, Junjie Huang +3

Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extract…

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