Zexiang Xu
Computer Vision and Pattern Recognition · 2018–2024
A researcher in the Science corpus: 11 papers; trusts 739 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 400 citations into it; #12,456 of 21,575 by trust here, #4,469 by citations
- Computer Graphics and Computer-Aided Design — 198 citations into it; #1,201 of 1,584 by trust here, #230 by citations
- Computational Mechanics — 164 citations into it; #7,741 of 10,001 by trust here, #1,160 by citations
- 3D Vision & Pose — 121 citations into it
- Aerospace Engineering — 64 citations into it
- Generative Models — 50 citations into it
- Artificial Intelligence — 45 citations into it
- Geology — 32 citations into it
Whom Zexiang Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Jimmy Ba
- Yann LeCun
- Ross B. Girshick
- Ilya Sutskever
- Karen Simonyan
Who trusts Zexiang Xu overall
- Kai Zhang
- Minghua Liu
- Anpei Chen
- Sai Bi
- Chandan Yeshwanth
- Jiahe Li
- Ang Cao
- Shenhan Qian
- Yinghao Xu
- Hao Tan
Papers from Zexiang Xu's perspective
Ordered by how strongly the researchers Zexiang Xu 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo (2021) — 1,072 citations
- Learning to reconstruct shape and spatially-varying reflectance from a single image (2018) — 325 citations
- OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets (2020) — 113 citations
- Point-NeRF: Point-based Neural Radiance Fields (2022) — 755 citations
- One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization (2023) — 717 citations
- Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model (2023) — 433 citations
- Single image portrait relighting (2019) — 306 citations
- GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting (2024) — 348 citations
- OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets (2020) — 47 citations
- Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (2019) — 378 citations
- TensoRF: Tensorial Radiance Fields (2022) — 1,785 citations