Yuting Su

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 451 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #7,183 of 21,575 by trust here, #3,909 by citations
• Artificial Intelligence — 87 citations into it; #10,814 of 35,345 by trust here, #6,266 by citations
• Biomedical Engineering — 41 citations into it
• Human-Computer Interaction — 28 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Computational Mechanics — 13 citations into it
• Optimization & Training — 12 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Yuting Su trusts overall

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

1. Ivan Laptev
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yurii E. Nesterov
6. Yoshua Bengio
7. Ross B. Girshick
8. Diederik P. Kingma
9. Yann LeCun
10. David G. Lowe

Who trusts Yuting Su overall

1. Gyuhak Kim
2. Yaoyao Liu
3. Jiangpeng He
4. Anan Liu
5. Zixuan Ke
6. Bernt Schiele
7. Ameya Prabhu
8. Jue Wang
9. Da-Wei Zhou
10. Han-Jia Ye

Papers from Yuting Su's perspective

Ordered by how strongly the researchers Yuting Su 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Mnemonics Training: Multi-Class Incremental Learning Without Forgetting (2020) — 413 citations
• Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition (2017) — 374 citations
• Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition (2017) — 108 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• Single/multi-view human action recognition via regularized multi-task learning (2015) — 56 citations
• Multiple/Single-View Human Action Recognition via Part-Induced Multitask Structural Learning (2015) — 111 citations
• Multi-modal & Multi-view & Interactive Benchmark Dataset for Human Action Recognition (2015) — 24 citations
• Coupled hidden conditional random fields for RGB-D human action recognition (2015) — 86 citations
• Clique-graph matching by preserving global & local structure (2015) — 60 citations