Ling-yu Duan

Computer Vision and Pattern Recognition · with Wang · 2008–2019

A researcher in the Science corpus: 11 papers; trusts 1,306 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 547 citations into it; #6,735 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 297 citations into it; #17,169 of 35,345 by trust here, #9,760 by citations
• Biomedical Engineering — 144 citations into it; #4,239 of 10,734 by trust here, #3,269 by citations
• Human-Computer Interaction — 110 citations into it; #2,123 of 2,253 by trust here, #1,041 by citations
• 3D Vision & Pose — 71 citations into it; #496 of 689 by trust here, #548 by citations
• Model Architectures — 66 citations into it; #1,321 of 2,278 by trust here, #1,210 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Evaluation & Benchmarks — 35 citations into it

Whom Ling-yu Duan trusts overall

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

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

Who trusts Ling-yu Duan overall

1. Jun Liu
2. Amir Shahroudy
3. Zehua Sun
4. G. Wang
5. Qiuhong Ke
6. Yangqing Jia

Papers from Ling-yu Duan's perspective

Ordered by how strongly the researchers Ling-yu Duan 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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

• Group-sensitive multiple kernel learning for object categorization (2009) — 173 citations
• NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019) — 1,730 citations
• Exploring Object Relation in Mean Teacher for Cross-Domain Detection (2019) — 348 citations
• Towards Accurate One-Stage Object Detection With AP-Loss (2019) — 130 citations
• Feature Boosting Network For 3D Pose Estimation (2019) — 129 citations
• Global Context-Aware Attention LSTM Networks for 3D Action Recognition (2017) — 655 citations
• VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild (2019) — 304 citations
• Skeleton-Based Online Action Prediction Using Scale Selection Network (2019) — 151 citations
• SSNet: Scale Selection Network for Online 3D Action Prediction (2018) — 55 citations
• Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (2017) — 539 citations
• A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams (2008) — 45 citations