Yu-Shen Liu

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 254 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it; #14,339 of 21,575 by trust here, #10,154 by citations
• Computational Mechanics — 69 citations into it; #6,446 of 10,001 by trust here, #3,190 by citations
• Geology — 36 citations into it; #455 of 687 by trust here, #324 by citations
• 3D Vision & Pose — 23 citations into it
• Aerospace Engineering — 22 citations into it
• Artificial Intelligence — 22 citations into it
• Model Architectures — 18 citations into it
• Generative Models — 14 citations into it

Whom Yu-Shen Liu trusts overall

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

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

Who trusts Yu-Shen Liu overall

1. Jonathan Sauder

Papers from Yu-Shen Liu's perspective

Ordered by how strongly the researchers Yu-Shen 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network (2018) — 359 citations
• SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (2021) — 340 citations
• 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation (2019) — 180 citations
• Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences (2018) — 62 citations
• View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions (2018) — 143 citations