Xingyuan Sun
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 6 papers; trusts 398 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 143 citations into it; #4,026 of 21,575 by trust here, #5,620 by citations
- 3D Vision & Pose — 52 citations into it; #257 of 689 by trust here, #243 by citations
- Aerospace Engineering — 52 citations into it
- Computational Mechanics — 50 citations into it; #2,585 of 10,001 by trust here, #1,050 by citations
- Artificial Intelligence — 46 citations into it
- Reinforcement Learning — 28 citations into it
- Computer Graphics and Computer-Aided Design — 26 citations into it
- Evaluation & Benchmarks — 19 citations into it
Whom Xingyuan Sun trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Ross B. Girshick
- Jitendra Malik
- Ilya Sutskever
- David G. Lowe
Who trusts Xingyuan Sun overall
- Jiajun Wu
- Georgia Gkioxari
- Jitendra Malik
- Xiuming Zhang
- Xian-Feng Han
- Zhoutong Zhang
- Chengkai Zhang
- Mike Roberts
- Jun-Yan Zhu
- Yonglong Tian
Papers from Xingyuan Sun's perspective
Ordered by how strongly the researchers Xingyuan Sun trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018) — 524 citations
- MarrNet: 3D Shape Reconstruction via 2.5D Sketches (2017) — 440 citations
- Learning to Infer and Execute 3D Shape Programs (2019) — 162 citations
- Spatial Action Maps for Mobile Manipulation (2020) — 88 citations
- A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation (2019) — 380 citations
- 3D Shape Perception from Monocular Vision, Touch, and Shape Priors (2018) — 133 citations