Xingang Pan

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 9 papers; trusts 1,119 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 300 citations into it; #7,749 of 21,575 by trust here, #4,502 by citations
• Artificial Intelligence — 117 citations into it; #26,855 of 35,345 by trust here, #15,236 by citations
• Generative Models — 96 citations into it
• Computer Graphics and Computer-Aided Design — 47 citations into it
• Representation & Self-Supervised Learning — 47 citations into it; #2,324 of 3,145 by trust here, #1,671 by citations
• Model Architectures — 26 citations into it; #2,013 of 2,278 by trust here, #1,643 by citations
• Evaluation & Benchmarks — 25 citations into it
• Optimization & Training — 18 citations into it

Whom Xingang Pan trusts overall

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

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

Who trusts Xingang Pan overall

1. Weihao Xia
2. Kaiyang Zhou
3. Ziwei Liu
4. Yuenan Hou
5. Lucas Tabelini
6. Chenlin Meng
7. Tu Zheng
8. Chen Change Loy
9. Yixiao Ge
10. Kwanyong Park

Papers from Xingang Pan's perspective

Ordered by how strongly the researchers Xingang Pan 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. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation (2020) — 448 citations
• Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs (2020) — 121 citations
• Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net (2018) — 943 citations
• Open Compound Domain Adaptation (2019) — 144 citations
• Spatial As Deep: Spatial CNN for Traffic Scene Understanding (2017) — 1,226 citations
• Self-Supervised Learning via Conditional Motion Propagation (2019) — 53 citations
• Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (2023) — 366 citations
• BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering (2021) — 323 citations
• Self-Supervised Scene De-Occlusion (2020) — 186 citations