Anan Liu

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 11 papers; trusts 577 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 220 citations into it; #7,149 of 21,575 by trust here, #3,848 by citations
• Artificial Intelligence — 109 citations into it; #10,721 of 35,345 by trust here, #6,193 by citations
• Biomedical Engineering — 42 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Human-Computer Interaction — 28 citations into it
• Computational Mechanics — 13 citations into it
• Optimization & Training — 13 citations into it
• Aerospace Engineering — 12 citations into it

Whom Anan Liu trusts overall

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

1. Ivan Laptev
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yurii E. Nesterov
6. Ross B. Girshick
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Karen Simonyan

Who trusts Anan Liu overall

1. Gyuhak Kim
2. Yaoyao Liu
3. Jiangpeng He
4. Fei Han
5. Pichao Wang
6. Jue Wang
7. Yinpeng Dong
8. Wanqing Li
9. Zixuan Ke
10. Jing Zhang

Papers from Anan Liu's perspective

Ordered by how strongly the researchers Anan 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. 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

• Mnemonics Training: Multi-Class Incremental Learning Without Forgetting (2020) — 413 citations
• Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition (2017) — 374 citations
• Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition (2017) — 108 citations
• Task-Adaptive Attention for Image Captioning (2021) — 326 citations
• MetaSearch: Incremental Product Search via Deep Meta-Learning (2020) — 63 citations
• RGB-D Object-to-CAD Retrieval (2018) — 18 citations
• Single/multi-view human action recognition via regularized multi-task learning (2015) — 56 citations
• Multiple/Single-View Human Action Recognition via Part-Induced Multitask Structural Learning (2015) — 111 citations
• Multi-modal & Multi-view & Interactive Benchmark Dataset for Human Action Recognition (2015) — 24 citations
• Coupled hidden conditional random fields for RGB-D human action recognition (2015) — 86 citations
• Clique-graph matching by preserving global & local structure (2015) — 60 citations