Hong Liu

Computer Vision and Pattern Recognition · Neural Networks · 2017–2021

A researcher in the Science corpus: 14 papers; trusts 1,496 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 388 citations into it; #4,355 of 21,575 by trust here, #4,317 by citations
• Artificial Intelligence — 138 citations into it
• Media Technology — 92 citations into it; #2,983 of 3,621 by trust here, #1,859 by citations
• Biomedical Engineering — 64 citations into it; #599 of 10,734 by trust here, #4,781 by citations
• Model Architectures — 62 citations into it
• Human-Computer Interaction — 58 citations into it; #217 of 2,253 by trust here, #604 by citations
• 3D Vision & Pose — 31 citations into it
• Evaluation & Benchmarks — 22 citations into it

Whom Hong Liu trusts overall

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

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

Who trusts Hong Liu overall

1. Yifan Zhang
2. Lei Shi
3. Chenyang Si
4. Pengfei Zhang
5. Jun Liu
6. Jian Cheng
7. Hyung-gun Chi
8. Pichao Wang
9. Hanqing Lu
10. Chaolong Li

Papers from Hong Liu's perspective

Ordered by how strongly the researchers Hong Liu 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Expectation-Maximization Attention Networks for Semantic Segmentation (2019) — 645 citations
• Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining (2018) — 892 citations
• Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation (2018) — 321 citations
• Enhanced skeleton visualization for view invariant human action recognition (2017) — 843 citations
• Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation (2021) — 302 citations
• Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition (2021) — 363 citations
• 3D action recognition using data visualization and convolutional neural networks (2017) — 17 citations
• Fusing shape and motion matrices for view invariant action recognition using 3D skeletons (2017) — 19 citations
• Transformer for Single Image Super-Resolution (2021) — 531 citations
• Efficient Transformer for Single Image Super-Resolution (2021) — 69 citations
• MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation (2021) — 408 citations
• Attention-guided CNN for image denoising (2020) — 620 citations
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020) — 359 citations
• TDD-net: a tiny defect detection network for printed circuit boards (2019) — 384 citations