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Ming-Yu Liu — most-cited papers & profile · AI for Code
← authors
·
overview
Ming-Yu Liu
46
papers ·
6186
citations ·
48
h-index
Wuhan Polytechnic University · Shandong University · National University of Defense Technology · Nanjing University of Science and Technology · State Key Laboratory of Microbial Technology · China Agricultural University
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Most-cited papers
High-resolution Image Synthesis And Semantic Manipulation With Conditional Gans
2017 · 4454 citations
Unsupervised Image-to-Image Translation Networks
2017 · 519 citations
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
2017 · 301 citations
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
2022 · 223 citations
Video-to-Video Synthesis
2018 · 127 citations
Coupled Generative Adversarial Networks
2016 · 102 citations
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
2017 · 96 citations
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
2018 · 86 citations
Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
2018 · 72 citations
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
2019 · 37 citations
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
2025 · 29 citations
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
2025 · 29 citations
NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning
2025 · 28 citations
CASENet: Deep Category-Aware Semantic Edge Detection
2017 · 25 citations
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
2020 · 22 citations
Top co-authors
Andrew Tao
· 1
Arushi Goel
· 1
Bryan Catanzaro
· 1
Dinesh Manocha
· 1
Ehsan Hosseini-Asl
· 1
Hanrong Ye
· 1
Jaehyeon Kim
· 1
Kaousheik Jayakumar
· 1
Lasha Koroshinadze
· 1
Mohammad Shoeybi
· 1
Nishit Anand
· 1
Pritam Biswas
· 1
Topics
Manipulation
Diffusion Models
Human-Robot Interaction
Sim-to-Real
Control
3D & NeRF Generation
Conditioning & Control
Video Understanding
GANs
Audio Generation