Feng Wang

Computer Vision and Pattern Recognition · with Chen · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 495 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it; #16,933 of 21,575 by trust here, #11,950 by citations
• Computer Graphics and Computer-Aided Design — 56 citations into it; #1,224 of 1,584 by trust here, #950 by citations
• Artificial Intelligence — 29 citations into it
• Generative Models — 24 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 6 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Feng Wang trusts overall

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

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

Who trusts Feng Wang overall

1. Yifan Zhang
2. Arka Pal
3. Shawn Shan
4. Chaoning Zhang
5. Rowan Zellers
6. Guikun Chen
7. Jiaxiang Tang
8. Bryan Hooi
9. Jenna Cryan
10. Deep Karkhanis

Papers from Feng Wang's perspective

Ordered by how strongly the researchers Feng Wang 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Understanding the Behaviour of Contrastive Loss (2020) — 894 citations
• Text-to-3D using Gaussian Splatting (2023) — 384 citations
• GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting (2023) — 432 citations