Xi Peng

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 947 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 200 citations into it; #10,799 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 118 citations into it; #28,434 of 35,345 by trust here, #9,275 by citations
• Representation & Self-Supervised Learning — 49 citations into it
• Human-Computer Interaction — 35 citations into it
• Generative Models — 34 citations into it
• Optimization & Training — 25 citations into it
• Model Architectures — 24 citations into it
• Control and Systems Engineering — 22 citations into it

Whom Xi Peng trusts overall

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

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

Who trusts Xi Peng overall

1. Long Zhao
2. Fengchun Qiao
3. Tao Hu
4. Ce Zheng
5. Linfeng Song
6. Yucheng Chen
7. Xinyao Wang
8. Jiaxing Huang
9. Zigang Geng
10. Zhiting Hu

Papers from Xi Peng's perspective

Ordered by how strongly the researchers Xi Peng 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. Long Short-Term Memory
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

• A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts (2017) — 402 citations
• Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation (2018) — 222 citations
• Learning to Learn Single Domain Generalization (2020) — 556 citations
• Semantic Graph Convolutional Networks for 3D Human Pose Regression (2019) — 621 citations
• SMIL: Multimodal Learning with Severely Missing Modality (2021) — 436 citations
• Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge (2020) — 73 citations