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researcher

Kun Zhang

13 papers hereh-index 13938 citations16 works total

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

author position
  • middle author4
  • last author9

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV5
  • q-bio.NC1
same name
  • Kun Zhang — 49 papers, h 48
  • Kun Zhang — 23 papers, h 19
  • Kun Zhang — 20 papers, h 17
  • Kun Zhang — 18 papers, h 17
  • Kun Zhang — 16 papers, h 4
  • Kun Zhang — 14 papers, h 6

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
20222024
most citedPLOT: Prompt Learning with Optimal Transport for Vision-Language Models

24 citations · 65 across the 13 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 6 cited

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model

Shaoan Xie, Zhifei Zhang, Zhe Lin +2

Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides…

q-bio.NC2022★ 5 cited

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

Yue Yu, Xuan Kan, Hejie Cui +9

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…

cs.LG2022★ 8 cited

Temporally Disentangled Representation Learning

Weiran Yao, Guangyi Chen, Kun Zhang

Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by expl…

cs.CV2022★ 24 cited

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Guangyi Chen, Weiran Yao, Xiangchen Song +3

With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike convention…

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