Yongming Rao

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 17 papers; trusts 1,846 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 606 citations into it; #7,665 of 21,575 by trust here, #3,476 by citations
• Artificial Intelligence — 300 citations into it
• Model Architectures — 120 citations into it
• Computational Mechanics — 106 citations into it; #4,870 of 10,001 by trust here, #2,715 by citations
• Generative Models — 84 citations into it
• Geology — 66 citations into it; #343 of 687 by trust here, #407 by citations
• Aerospace Engineering — 59 citations into it
• Representation & Self-Supervised Learning — 54 citations into it

Whom Yongming Rao trusts overall

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

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

Who trusts Yongming Rao overall

1. Sachin Mehta
2. Fabian B. Fuchs
3. Weihao Yu
4. Tian Zhou
5. Yizeng Han
6. Ji Lin
7. Yulin Wang
8. Jianwei Yang
9. Xueyan Zou
10. Yanyu Li

Papers from Yongming Rao's perspective

Ordered by how strongly the researchers Yongming Rao 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis (2019) — 448 citations
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting (2021) — 830 citations
• Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling (2021) — 1,136 citations
• DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification (2021) — 1,174 citations
• Global Filter Networks for Image Classification (2021) — 726 citations
• Unleashing Text-to-Image Diffusion Models for Visual Perception (2023) — 350 citations
• Attention-Aware Deep Reinforcement Learning for Video Face Recognition (2017) — 171 citations
• Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition (2019) — 170 citations
• Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds (2020) — 150 citations
• HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions (2022) — 401 citations
• UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models (2023) — 482 citations
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion (2022) — 402 citations
• Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement (2022) — 34 citations
• Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification (2021) — 353 citations
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers (2021) — 632 citations
• Structure-Preserving Super Resolution With Gradient Guidance (2020) — 388 citations
• Runtime Neural Pruning (2017) — 528 citations