Tiejun Huang

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 16 papers; trusts 1,595 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 548 citations into it; #12,356 of 21,575 by trust here, #3,323 by citations
• Artificial Intelligence — 393 citations into it; #30,578 of 35,345 by trust here, #7,310 by citations
• Model Architectures — 124 citations into it
• Multimodal Learning — 67 citations into it
• Evaluation & Benchmarks — 66 citations into it
• Representation & Self-Supervised Learning — 64 citations into it
• Large Language Models — 59 citations into it
• Optimization & Training — 57 citations into it

Whom Tiejun Huang trusts overall

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

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

Who trusts Tiejun Huang overall

1. Man Yao
2. Afshin Oroojlooyjadid
3. Mustaqeem

Papers from Tiejun Huang's perspective

Ordered by how strongly the researchers Tiejun Huang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• EVA: Exploring the Limits of Masked Visual Representation Learning at Scale (2022) — 1,015 citations
• Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling (2021) — 1,136 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
• EVA-02: A Visual Representation for Neon Genesis (2023) — 551 citations
• Bi-Directional Cascade Network for Perceptual Edge Detection (2019) — 353 citations
• Images Speak in Images: A Generalist Painter for In-Context Visual Learning (2022) — 387 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
• 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
• SVIT: Scaling up Visual Instruction Tuning (2023) — 176 citations
• Graph Convolutional Reinforcement Learning (2018) — 435 citations
• Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018) — 385 citations
• Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018) — 305 citations
• Cross-Domain Adversarial Feature Learning for Sketch Re-identification (2018) — 75 citations