Yue Gao

Computer Vision and Pattern Recognition · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2012–2025

A researcher in the Science corpus: 16 papers; trusts 794 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 262 citations into it; #12,921 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 119 citations into it; #34,028 of 35,345 by trust here, #11,192 by citations
• Computational Mechanics — 51 citations into it; #8,708 of 10,001 by trust here, #3,652 by citations
• Graph Learning — 34 citations into it
• Model Architectures — 29 citations into it
• Aerospace Engineering — 28 citations into it
• Geology — 27 citations into it; #584 of 687 by trust here, #392 by citations
• Representation & Self-Supervised Learning — 23 citations into it

Whom Yue Gao trusts overall

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

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

Who trusts Yue Gao overall

1. Yifan Feng
2. Zhenyun Deng
3. Ming-Ming Cheng
4. Chao-qun Hong
5. Anan Liu
6. Yuxing Sun
7. Lianghao Xia
8. Saeed Rahmani
9. Jun Yu
10. Lu Zhang

Papers from Yue Gao's perspective

Ordered by how strongly the researchers Yue Gao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering (2020) — 131 citations
• GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition (2018) — 623 citations
• Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (2013) — 463 citations
• 3-D Object Retrieval and Recognition With Hypergraph Analysis (2012) — 627 citations
• YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception (2025) — 401 citations
• Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation (2024) — 332 citations
• HGNN+: General Hypergraph Neural Networks (2022) — 444 citations
• IExpressNet: Facial Expression Recognition with Incremental Classes (2020) — 22 citations
• Hypergraph Learning: Methods and Practices (2020) — 378 citations
• Deep Multi-View Enhancement Hashing for Image Retrieval (2020) — 443 citations
• Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019) — 17 citations
• Group-Wise Learning for Aurora Image Classification With Multiple Representations (2019) — 11 citations
• Dynamic Hypergraph Neural Networks (2019) — 429 citations
• PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition (2018) — 191 citations
• MeshNet: Mesh Neural Network for 3D Shape Representation (2018) — 367 citations
• Hypergraph Neural Networks (2018) — 2,070 citations