Xiaochun Cao

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 23 papers; trusts 1,268 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 515 citations into it; #3,842 of 21,575 by trust here, #2,435 by citations
• Artificial Intelligence — 246 citations into it; #25,752 of 35,345 by trust here, #12,974 by citations
• Media Technology — 113 citations into it; #1,019 of 3,621 by trust here, #353 by citations
• Computational Mechanics — 56 citations into it
• Probabilistic & Bayesian Methods — 55 citations into it
• Signal Processing — 43 citations into it
• Radiology, Nuclear Medicine and Imaging — 34 citations into it
• Representation & Self-Supervised Learning — 31 citations into it; #2,908 of 3,145 by trust here, #3,089 by citations

Whom Xiaochun Cao trusts overall

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

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

Who trusts Xiaochun Cao overall

1. Changqing Zhang
2. Wenguan Wang
3. Huan Fu
4. Dingwen Zhang
5. Jianlong Wu
6. Yan Yang
7. Yuan Xie
8. Shirui Luo
9. Jianbing Shen
10. Handong Zhao

Papers from Xiaochun Cao's perspective

Ordered by how strongly the researchers Xiaochun Cao 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Audio Visual Attribute Discovery for Fine-Grained Object Recognition (2018) — 26 citations
• Single Image Dehazing via Multi-scale Convolutional Neural Networks (2016) — 1,713 citations
• Deep People Counting in Extremely Dense Crowds (2015) — 384 citations
• Correction to: Single Image Super-Resolution via a Holistic Attention Network (2020) — 825 citations
• Single Image Deraining: A Comprehensive Benchmark Analysis (2019) — 358 citations
• Gated Fusion Network for Single Image Dehazing (2018) — 881 citations
• Image Deblurring via Extreme Channels Prior (2017) — 358 citations
• Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014) — 45 citations
• Low-Rank Tensor Constrained Multiview Subspace Clustering (2015) — 487 citations
• Cluster-based Co-saliency Detection (2013) — 411 citations
• Low-Light Image Enhancement via a Deep Hybrid Network (2019) — 460 citations
• ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples (2018) — 331 citations
• Diversity-induced Multi-view Subspace Clustering (2015) — 690 citations
• Depth Enhanced Saliency Detection Method (2014) — 360 citations
• Generalized Latent Multi-View Subspace Clustering (2020) — 650 citations
• Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (2020) — 117 citations
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020) — 2,678 citations
• Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019) — 361 citations
• Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018) — 836 citations
• Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image (2018) — 404 citations