Chunhong Pan

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 18 papers; trusts 1,406 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 447 citations into it; #7,531 of 21,575 by trust here, #3,404 by citations
• Artificial Intelligence — 312 citations into it
• Model Architectures — 118 citations into it
• Computational Mechanics — 68 citations into it; #9,903 of 10,001 by trust here, #2,509 by citations
• Optimization & Training — 60 citations into it
• Neural Architecture Search & AutoML — 52 citations into it; #249 of 283 by trust here, #122 by citations
• Representation & Self-Supervised Learning — 45 citations into it; #2,457 of 3,145 by trust here, #2,330 by citations
• Geology — 40 citations into it; #679 of 687 by trust here, #300 by citations

Whom Chunhong Pan trusts overall

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

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

Who trusts Chunhong Pan overall

1. Yuxin Peng
2. Wenqi Ren
3. Jun Yue

Papers from Chunhong Pan's perspective

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

• Deep Adaptive Image Clustering (2017) — 553 citations
• DetNAS: Neural Architecture Search on Object Detection (2019) — 94 citations
• Efficient Image Dehazing with Boundary Constraint and Contextual Regularization (2013) — 1,081 citations
• Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images (2017) — 185 citations
• Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection (2018) — 130 citations
• You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization (2018) — 100 citations
• Discriminative Least Squares Regression for Multiclass Classification and Feature Selection (2012) — 432 citations
• Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval (2015) — 213 citations
• Differentiable Architecture Search with Ensemble Gumbel-Softmax (2019) — 20 citations
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020) — 301 citations
• DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling (2020) — 15 citations
• Relation-Shape Convolutional Neural Network for Point Cloud Analysis (2019) — 1,032 citations
• AugFPN: Improving Multi-Scale Feature Learning for Object Detection (2019) — 495 citations
• DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (2019) — 299 citations
• DATA: Differentiable ArchiTecture Approximation (2019) — 56 citations
• Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network (2017) — 386 citations
• Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks (2014) — 598 citations
• Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (2014) — 34 citations