Rui Huang
Computer Vision and Pattern Recognition · IEEE Transactions on Image Processing · 2011–2021
A researcher in the Science corpus: 10 papers; trusts 886 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 294 citations into it; #6,238 of 21,575 by trust here, #5,680 by citations
- Artificial Intelligence — 190 citations into it; #9,991 of 35,345 by trust here, #16,166 by citations
- Model Architectures — 81 citations into it
- Efficiency & Compression — 59 citations into it; #1,059 of 1,264 by trust here, #987 by citations
- Optimization & Training — 31 citations into it
- Evaluation & Benchmarks — 22 citations into it
- Safety, Robustness & Interpretability — 21 citations into it
- Electrical and Electronic Engineering — 18 citations into it
Whom Rui Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Jia Deng
- Yoshua Bengio
- Yann LeCun
- Ross B. Girshick
- Ilya Sutskever
- Karen Simonyan
Who trusts Rui Huang overall
- Yiyou Sun
- Haoqi Wang
- Yixuan Li
- Jingkang Yang
- Zhizhong Li
- Zhen Fang
- Yifei Ming
- Chuan Guo
- Hongxin Wei
- Litong Feng
Papers from Rui Huang's perspective
Ordered by how strongly the researchers Rui Huang 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
- Going deeper with convolutions
Their papers
- Learning Dynamic Siamese Network for Visual Object Tracking (2017) — 757 citations
- Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification (2020) — 172 citations
- On the Importance of Gradients for Detecting Distributional Shifts in the Wild (2021) — 498 citations
- MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space (2021) — 332 citations
- Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition (2021) — 264 citations
- Loss odyssey in medical image segmentation (2021) — 592 citations
- Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length (2021) — 48 citations
- Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion (2020) — 383 citations
- CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation (2020) — 655 citations
- A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI (2011) — 1,311 citations