Zhiqin Chen
Computer Vision and Pattern Recognition · 2018–2022
A researcher in the Science corpus: 3 papers; trusts 607 researchers and is trusted by 81 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 95 citations into it; #18,712 of 21,575 by trust here, #15,955 by citations
- Computational Mechanics — 68 citations into it; #5,626 of 10,001 by trust here, #4,039 by citations
- Computer Graphics and Computer-Aided Design — 57 citations into it; #903 of 1,584 by trust here, #746 by citations
- 3D Vision & Pose — 30 citations into it; #57 of 689 by trust here, #161 by citations
- Generative Models — 25 citations into it; #219 of 1,956 by trust here, #569 by citations
- Geology — 17 citations into it
- Aerospace Engineering — 13 citations into it
- Artificial Intelligence — 11 citations into it
Whom Zhiqin Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Charles Ruizhongtai Qi
- Hao Su
- Yann LeCun
- Ross B. Girshick
- Ilya Sutskever
Who trusts Zhiqin Chen overall
- Anpei Chen
- Michael Niemeyer
- Songyou Peng
- Julian Chibane
- Rundi Wu
- Zehao Yu
- Andreas Geiger
- Christian Reiser
- Alex Yu
- Matan Atzmon
Papers from Zhiqin Chen's perspective
Ordered by how strongly the researchers Zhiqin Chen 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures (2022) — 454 citations
- BSP-Net: Generating Compact Meshes via Binary Space Partitioning (2019) — 362 citations
- Learning Implicit Fields for Generative Shape Modeling (2018) — 1,852 citations