Jian Yao

Computer Vision and Pattern Recognition · Neurocomputing · 2007–2019

A researcher in the Science corpus: 7 papers; trusts 838 researchers and is trusted by 826 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it; #1,513 of 21,575 by trust here, #4,402 by citations
• Artificial Intelligence — 51 citations into it; #4,214 of 35,345 by trust here, #8,157 by citations
• Model Architectures — 24 citations into it; #361 of 2,278 by trust here, #785 by citations
• Natural Language Processing — 21 citations into it
• Media Technology — 20 citations into it
• Aerospace Engineering — 19 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Multimodal Learning — 7 citations into it

Whom Jian Yao trusts overall

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

1. Geoffrey E. Hinton
2. Ross B. Girshick
3. Kaiming He
4. David G. Lowe
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Diederik P. Kingma
9. Christian Szegedy
10. Andrew Zisserman

Who trusts Jian Yao overall

1. Anurag Arnab
2. Sanja Fidler
3. Raquel Urtasun
4. Liang-Chieh Chen
5. Philip H. S. Torr
6. Jan Hendrik Hosang
7. Min Sun
8. Yukun Zhu
9. Ziyu Zhang
10. Alexander Gerhard Schwing

Papers from Jian Yao's perspective

Ordered by how strongly the researchers Jian Yao 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. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation (2012) — 449 citations
• Text-Attentional Convolutional Neural Network for Scene Text Detection (2015) — 321 citations
• Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs (2013) — 37 citations
• Regularizing RNNs for Caption Generation by Reconstructing the Past with the Present (2018) — 97 citations
• DeepCrack: A deep hierarchical feature learning architecture for crack segmentation (2019) — 1,037 citations
• A Unified Framework for Street-View Panorama Stitching (2016) — 656 citations
• Multi-Layer Background Subtraction Based on Color and Texture (2007) — 311 citations