Xiaohui Zeng

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 376 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 158 citations into it; #16,789 of 21,575 by trust here, #5,680 by citations
• Generative Models — 71 citations into it
• Artificial Intelligence — 64 citations into it
• Computer Graphics and Computer-Aided Design — 54 citations into it; #1,396 of 1,584 by trust here, #298 by citations
• Multimodal Learning — 24 citations into it
• Natural Language Processing — 18 citations into it
• Optimization & Training — 7 citations into it

Whom Xiaohui Zeng trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Danilo Jimenez Rezende
10. Radford M. Neal

Who trusts Xiaohui Zeng overall

1. Alex Nichol
2. Heewoo Jun
3. Minghua Liu
4. Kevin Clark
5. Haochen Wang
6. Ali Athar
7. Yuxi Li
8. Kevin Black
9. Xinkai Lu
10. Taoran Yi

Papers from Xiaohui Zeng's perspective

Ordered by how strongly the researchers Xiaohui Zeng 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Magic3D: High-Resolution Text-to-3D Content Creation (2022) — 1,645 citations
• LION: Latent Point Diffusion Models for 3D Shape Generation (2022) — 720 citations
• DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation (2019) — 77 citations
• Scene Graph Parsing as Dependency Parsing (2018) — 59 citations