Qian Zhao

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 10 papers; trusts 609 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 182 citations into it; #9,414 of 21,575 by trust here, #8,502 by citations
• Artificial Intelligence — 85 citations into it; #24,736 of 35,345 by trust here, #31,361 by citations
• Media Technology — 61 citations into it; #2,553 of 3,621 by trust here, #1,242 by citations
• Computational Mechanics — 60 citations into it; #6,932 of 10,001 by trust here, #7,884 by citations
• Optimization & Training — 42 citations into it; #2,066 of 2,601 by trust here, #1,248 by citations
• Signal Processing — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Statistics and Probability — 16 citations into it

Whom Qian Zhao trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. E.J. Candes
7. Yann LeCun
8. Ilya Sutskever
9. Radford M. Neal
10. Ross B. Girshick

Who trusts Qian Zhao overall

1. Lu Jiang
2. Haw-Shiuan Chang
3. Jie Xiao
4. Chong Mou
5. Syed Waqas Zamir
6. Zhendong Wang
7. Mrinmaya Sachan
8. Deyu Meng
9. Eric Learned-Miller
10. Zhengyuan Zhou

Papers from Qian Zhao's perspective

Ordered by how strongly the researchers Qian Zhao 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Variational Denoising Network: Toward Blind Noise Modeling and Removal (2019) — 374 citations
• A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection (2015) — 136 citations
• What Objective Does Self-paced Learning Indeed Optimize? (2015) — 80 citations
• A Model-Driven Deep Neural Network for Single Image Rain Removal (2020) — 429 citations
• Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (2019) — 1,031 citations
• Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018) — 313 citations
• Semi-Supervised Transfer Learning for Image Rain Removal (2018) — 463 citations
• Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017) — 453 citations
• Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization (2016) — 65 citations
• Self-Paced Curriculum Learning (2015) — 591 citations