Yonghong Tian

Computer Vision and Pattern Recognition · 2009–2023

A researcher in the Science corpus: 20 papers; trusts 1,675 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 651 citations into it; #6,954 of 21,575 by trust here, #3,426 by citations
• Artificial Intelligence — 504 citations into it; #32,312 of 35,345 by trust here, #7,471 by citations
• Representation & Self-Supervised Learning — 134 citations into it
• Model Architectures — 133 citations into it
• Electrical and Electronic Engineering — 119 citations into it; #11,628 of 16,539 by trust here, #9,902 by citations
• Multimodal Learning — 93 citations into it
• Cognitive Neuroscience — 84 citations into it; #7,839 of 10,852 by trust here, #6,360 by citations
• Natural Language Processing — 76 citations into it

Whom Yonghong Tian trusts overall

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

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

Who trusts Yonghong Tian overall

1. Liang Zheng
2. Yangqing Jia
3. Yuhui Yuan
4. Dongliang He
5. Man Yao
6. Siyuan Li
7. Yifan Sun
8. Xinchen Liu
9. Zehua Sun
10. Yongming Rao

Papers from Yonghong Tian's perspective

Ordered by how strongly the researchers Yonghong Tian 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Group-sensitive multiple kernel learning for object categorization (2009) — 173 citations
• PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation (2021) — 243 citations
• Deep Transfer Learning for Person Re-Identification (2016) — 314 citations
• Deep Relative Distance Learning: Tell the Difference between Similar Vehicles (2016) — 738 citations
• Unsupervised Cross-Dataset Transfer Learning for Person Re-identification (2016) — 388 citations
• Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network (2016) — 65 citations
• Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning (2019) — 197 citations
• Adversarial Reciprocal Points Learning for Open Set Recognition (2021) — 466 citations
• Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark (2021) — 297 citations
• Deep Residual Learning in Spiking Neural Networks (2021) — 793 citations
• Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (2020) — 776 citations
• Attention Spiking Neural Networks (2022) — 303 citations
• Spikformer: When Spiking Neural Network Meets Transformer (2022) — 519 citations
• Spike-driven Transformer (2023) — 315 citations
• Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey (2023) — 326 citations
• Masked Autoencoders for Point Cloud Self-supervised Learning (2022) — 737 citations
• Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition (2020) — 19 citations
• Compositional Few-Shot Recognition with Primitive Discovery and Enhancing (2020) — 33 citations
• HRank: Filter Pruning Using High-Rank Feature Map (2020) — 899 citations
• Cross-Domain Adversarial Feature Learning for Sketch Re-identification (2018) — 75 citations