Sen Wang

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 10 papers; trusts 701 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 238 citations into it; #5,929 of 21,575 by trust here, #5,422 by citations
• Computational Mechanics — 77 citations into it
• Aerospace Engineering — 70 citations into it; #1,654 of 6,421 by trust here, #1,815 by citations
• Artificial Intelligence — 56 citations into it
• 3D Vision & Pose — 55 citations into it
• Generative Models — 49 citations into it
• Geology — 46 citations into it
• Computer Graphics and Computer-Aided Design — 31 citations into it

Whom Sen Wang trusts overall

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

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

Who trusts Sen Wang overall

1. Devendra Singh Chaplot
2. Andrew J. Davison
3. João F. Henriques
4. Emilio Parisotto
5. Ruihao Li
6. Nan Yang
7. Muhamad Risqi U. Saputra
8. Andrea Vedaldi
9. Youqi Pan
10. Chuan Guo

Papers from Sen Wang's perspective

Ordered by how strongly the researchers Sen Wang 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

• DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks (2017) — 916 citations
• VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem (2017) — 401 citations
• Action2Motion: Conditioned Generation of 3D Human Motions (2020) — 642 citations
• Generating Diverse and Natural 3D Human Motions from Text (2022) — 1,034 citations
• 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations (2018) — 28 citations
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017) — 208 citations
• MoMask: Generative Masked Modeling of 3D Human Motions (2023) — 445 citations
• TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts (2022) — 433 citations
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019) — 390 citations
• Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction (2018) — 156 citations