Jinjin Gu

Computer Vision and Pattern Recognition · 2018–2024

A researcher in the Science corpus: 8 papers; trusts 1,322 researchers and is trusted by 389 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 345 citations into it; #2,075 of 21,575 by trust here, #2,091 by citations
• Media Technology — 166 citations into it; #1,597 of 3,621 by trust here, #1,329 by citations
• Generative Models — 116 citations into it; #201 of 1,956 by trust here, #279 by citations
• Artificial Intelligence — 78 citations into it
• Evaluation & Benchmarks — 39 citations into it
• Model Architectures — 27 citations into it
• Safety, Robustness & Interpretability — 18 citations into it
• Biophysics — 17 citations into it

Whom Jinjin Gu trusts overall

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

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

Who trusts Jinjin Gu overall

1. Kai Zhang
2. Yuval Alaluf
3. Yujun Shen
4. Zongze Wu
5. Weihao Xia
6. Chenlin Meng
7. Xintao Wang
8. Chao Dong
9. Tao Yang
10. Jianyi Wang

Papers from Jinjin Gu's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Interpreting the Latent Space of GANs for Semantic Face Editing (2019) — 1,258 citations
• Image Processing Using Multi-Code GAN Prior (2019) — 345 citations
• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (2018) — 4,766 citations
• Blind Super-Resolution With Iterative Kernel Correction (2019) — 571 citations
• Interpreting Super-Resolution Networks with Local Attribution Maps (2020) — 353 citations
• PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration (2020) — 298 citations
• Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild (2024) — 328 citations
• Dual Aggregation Transformer for Image Super-Resolution (2023) — 382 citations