Hao Tang

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 541 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #18,397 of 21,575 by trust here, #11,721 by citations
• Generative Models — 55 citations into it
• Artificial Intelligence — 42 citations into it
• Multimodal Learning — 25 citations into it
• Media Technology — 22 citations into it; #2,984 of 3,621 by trust here, #2,280 by citations
• Model Architectures — 21 citations into it
• Optimization & Training — 12 citations into it
• Natural Language Processing — 7 citations into it

Whom Hao Tang trusts overall

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

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

Who trusts Hao Tang overall

1. Weihao Xia
2. Yufan Zhou
3. Zhu Zhang
4. Ming Ding
5. Ibraheem Alhashim
6. Dong Huk Park
7. Nupur Kumari
8. Yujiu Yang
9. Aditya Ramesh

Papers from Hao Tang's perspective

Ordered by how strongly the researchers Hao Tang 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

• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (2020) — 308 citations
• Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation (2018) — 321 citations
• MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation (2021) — 408 citations
• DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis (2020) — 224 citations