Shuyang Zhao

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 501 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #9,771 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 22 citations into it
• Cognitive Neuroscience — 19 citations into it; #10,585 of 10,852 by trust here, #6,360 by citations
• Signal Processing — 19 citations into it; #6,965 of 7,572 by trust here, #7,112 by citations
• Sensory Systems — 12 citations into it
• Model Architectures — 9 citations into it
• Multimodal Learning — 6 citations into it
• Developmental Biology — 5 citations into it

Whom Shuyang Zhao trusts overall

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

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

Who trusts Shuyang Zhao overall

1. Xiaoqi Zhao
2. Junhang Wei
3. Youwei Pang
4. Wenguan Wang
5. Deng-Ping Fan
6. Toni Heittola
7. Keren Fu
8. Eduardo Fonseca
9. Shuhui Wang

Papers from Shuyang Zhao's perspective

Ordered by how strongly the researchers Shuyang Zhao 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Unsupervised Video Object Segmentation Through Visual Attention (2019) — 224 citations
• Salient Object Detection With Pyramid Attention and Salient Edges (2019) — 477 citations
• Active Learning for Sound Event Detection (2020) — 38 citations
• An Active Learning Method Using Clustering and Committee-Based Sample Selection for Sound Event Classification (2018) — 14 citations