Qi Zhao

Computer Vision and Pattern Recognition · with Kankanhalli · 2013–2022

A researcher in the Science corpus: 10 papers; trusts 1,324 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 310 citations into it; #4,992 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 156 citations into it; #24,603 of 35,345 by trust here, #10,667 by citations
• Cognitive Neuroscience — 51 citations into it; #5,935 of 10,852 by trust here, #4,022 by citations
• Representation & Self-Supervised Learning — 47 citations into it
• Optimization & Training — 34 citations into it
• Model Architectures — 29 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Human-Computer Interaction — 21 citations into it

Whom Qi Zhao trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Yoshua Bengio
7. David G. Lowe
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Qi Zhao overall

1. Wenguan Wang
2. Jianbing Shen
3. Marcella Cornia
4. Junting Pan
5. Matthias Kümmerer
6. Srinivas S. S. Kruthiventi
7. Yusuke Sugano
8. Ruohan Zhang
9. Lorenzo Baraldi
10. Drew Linsley

Papers from Qi Zhao's perspective

Ordered by how strongly the researchers Qi 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• SALICON: Saliency in Context (2015) — 756 citations
• SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks (2015) — 605 citations
• Predicting human gaze beyond pixels. (2014) — 360 citations
• Learning to Learn From Noisy Labeled Data (2018) — 372 citations
• Foveation-based Mechanisms Alleviate Adversarial Examples (2015) — 179 citations
• Learning to Share Latent Tasks for Action Recognition (2013) — 60 citations
• Attention Transfer from Web Images for Video Recognition (2017) — 47 citations
• Attentive Systems: A Survey (2017) — 71 citations
• Learning to Predict Gradients for Semi-Supervised Continual Learning (2022) — 14 citations
• TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios (2021) — 1,732 citations