Yandong Li

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 664 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 172 citations into it; #21,037 of 21,575 by trust here, #6,542 by citations
• Artificial Intelligence — 162 citations into it; #32,003 of 35,345 by trust here, #9,641 by citations
• Model Architectures — 45 citations into it
• Safety, Robustness & Interpretability — 34 citations into it
• Multimodal Learning — 28 citations into it; #1,185 of 1,211 by trust here, #846 by citations
• Natural Language Processing — 23 citations into it
• Optimization & Training — 12 citations into it
• Signal Processing — 12 citations into it

Whom Yandong Li trusts overall

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

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

Who trusts Yandong Li overall

1. Avishek Joey Bose
2. Zhichao Huang
3. Bo Liu
4. Runtian Zhai
5. Tong Zhang
6. Andre Cianflone
7. Bo Wu
8. Tingting Qiao
9. Jiayuan Mao

Papers from Yandong Li's perspective

Ordered by how strongly the researchers Yandong Li 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification (2017) — 49 citations
• VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation (2017) — 137 citations
• StNet: Local and Global Spatial-Temporal Modeling for Action Recognition (2018) — 145 citations
• MoViNets: Mobile Video Networks for Efficient Video Recognition (2021) — 299 citations
• NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks (2019) — 273 citations