Di Wu

Computer Vision and Pattern Recognition · with Shao · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 240 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 68 citations into it; #4,101 of 21,575 by trust here, #6,671 by citations
• Artificial Intelligence — 27 citations into it; #16,635 of 35,345 by trust here, #21,316 by citations
• Human-Computer Interaction — 19 citations into it; #673 of 2,253 by trust here, #604 by citations
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 6 citations into it
• Developmental and Educational Psychology — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Di Wu trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Ivan Laptev
4. Antonio Torralba
5. Alex Krizhevsky
6. Yoshua Bengio
7. Navneet Dalal
8. Chih-Jen Lin
9. Yann LeCun
10. Ilya Sutskever

Who trusts Di Wu overall

1. Pichao Wang
2. Yong Du
3. Wanqing Li
4. Guillermo Garcia-Hernando
5. Runpeng Cui
6. Behrooz Mahasseni
7. Liliana Lo Presti
8. Qiuhong Ke
9. Liang Wang
10. Jun Wan

Papers from Di Wu's perspective

Ordered by how strongly the researchers Di Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition (2014) — 226 citations
• Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition (2016) — 437 citations
• One shot learning gesture recognition from RGBD images (2012) — 151 citations