Li Liu

Computer Vision and Pattern Recognition · 13 papers · 2013–2020

A researcher in the Science corpus: 13 papers; trusts 1,596 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 515 citations into it; #6,141 of 21,575 by trust here, #5,711 by citations
• Artificial Intelligence — 261 citations into it; #14,473 of 35,345 by trust here, #10,566 by citations
• Optimization & Training — 101 citations into it
• Aerospace Engineering — 68 citations into it; #2,160 of 6,421 by trust here, #2,509 by citations
• Representation & Self-Supervised Learning — 60 citations into it; #778 of 3,145 by trust here, #1,692 by citations
• Model Architectures — 58 citations into it
• Media Technology — 42 citations into it
• Radiology, Nuclear Medicine and Imaging — 31 citations into it

Whom Li Liu trusts overall

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

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

Who trusts Li Liu overall

1. Ayan Kumar Bhunia
2. Aneeshan Sain
3. Anjan Dutta
4. Xiaodong Yang
5. Shiv Ram Dubey
6. P. PramodKumar
7. Yi-Zhe Song
8. Kaiyue Pang
9. Mengyang Yu
10. Jianfang Hu

Papers from Li Liu's perspective

Ordered by how strongly the researchers Li Liu 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. Attention is All you Need (Neural Information Processing Systems)
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

• Normalization Techniques in Training DNNs: Methodology, Analysis and Application (2020) — 457 citations
• Attentive Region Embedding Network for Zero-Shot Learning (2019) — 297 citations
• Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval (2017) — 298 citations
• Zero-Shot Sketch-Image Hashing (2018) — 172 citations
• Region Graph Embedding Network for Zero-Shot Learning (2020) — 179 citations
• Structure-Preserving Binary Representations for RGB-D Action Recognition (2016) — 116 citations
• Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018) — 372 citations
• Feature Learning for Image Classification Via Multiobjective Genetic Programming (2014) — 297 citations
• STAR: A Structure and Texture Aware Retinex Model (2019) — 297 citations
• Binary Multi-View Clustering (2019) — 542 citations
• Iterative Normalization: Beyond Standardization Towards Efficient Whitening (2019) — 188 citations
• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016) — 145 citations
• Learning Discriminative Representations from RGB-D Video Data (2013) — 304 citations