Yijun Li

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 11 papers; trusts 1,301 researchers and is trusted by 96 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 374 citations into it; #3,334 of 21,575 by trust here, #3,057 by citations
• Generative Models — 199 citations into it; #908 of 1,956 by trust here, #748 by citations
• Artificial Intelligence — 106 citations into it; #22,352 of 35,345 by trust here, #17,776 by citations
• Computer Graphics and Computer-Aided Design — 52 citations into it; #1,583 of 1,584 by trust here, #486 by citations
• Representation & Self-Supervised Learning — 39 citations into it; #3,078 of 3,145 by trust here, #2,624 by citations
• Media Technology — 22 citations into it; #3,535 of 3,621 by trust here, #2,479 by citations
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 19 citations into it

Whom Yijun Li trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Andrew Zisserman
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Yijun Li overall

1. Yongcheng Jing
2. Jiahui Yu
3. Stephan R. Richter
4. Levent Karacan
5. Wei-Sheng Lai
6. Chao Yang
7. Huikai Wu
8. Eli Shechtman
9. Richard Zhang
10. Jaejun Yoo

Papers from Yijun Li's perspective

Ordered by how strongly the researchers Yijun Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Universal Style Transfer via Feature Transforms (2017) — 1,142 citations
• A Closed-form Solution to Photorealistic Image Stylization (2018) — 447 citations
• Flow-Grounded Spatial-Temporal Video Prediction from Still Images (2018) — 142 citations
• Generative Face Completion (2017) — 631 citations
• Zero-shot Image-to-Image Translation (2023) — 653 citations
• Deep Joint Image Filtering (2016) — 297 citations
• Few-shot Image Generation via Cross-domain Correspondence (2021) — 318 citations
• Few-shot Image Generation with Elastic Weight Consolidation (2020) — 190 citations
• Collaging Class-specific GANs for Semantic Image Synthesis (2021) — 36 citations
• Contrastive Learning for Diverse Disentangled Foreground Generation (2022) — 8 citations
• Rethinking and Improving the Robustness of Image Style Transfer (2021) — 108 citations