Di Huang

Computer Vision and Pattern Recognition · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 2011–2020

A researcher in the Science corpus: 6 papers; trusts 1,154 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 200 citations into it; #5,326 of 21,575 by trust here, #5,287 by citations
• Artificial Intelligence — 94 citations into it; #15,182 of 35,345 by trust here, #15,004 by citations
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Optimization & Training — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 9 citations into it; #2,235 of 3,265 by trust here, #2,400 by citations
• Media Technology — 8 citations into it
• Efficiency & Compression — 5 citations into it

Whom Di Huang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Christian Szegedy
7. Shaoqing Ren
8. Tsung-Yi Lin
9. Andrew Zisserman
10. X. Zhang

Who trusts Di Huang overall

1. Songtao Liu
2. Anne Humeau-Heurtier
3. Li Liu
4. Zheng Ge
5. Juan R. Terven
6. Jiayi Ma
7. Xingchen Ma
8. Momina Liaqat Ali
9. Xiaodong Cun
10. Xun Cheng

Papers from Di Huang's perspective

Ordered by how strongly the researchers Di Huang 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Receptive Field Block Net for Accurate and Fast Object Detection (2017) — 1,541 citations
• Adaptive NMS: Refining Pedestrian Detection in a Crowd (2019) — 340 citations
• Multi-Scale Positive Sample Refinement for Few-Shot Object Detection (2020) — 363 citations
• Learning Spatial Fusion for Single-Shot Object Detection (2019) — 863 citations
• Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (2011) — 969 citations
• DepAudioNet: An Efficient Deep Model for Audio based Depression Classification (2016) — 298 citations