Yue Huang

Computer Vision and Pattern Recognition · with Liu · 2024–2026

A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Yue Huang trusts

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

  1. Kaiming He (in Computer Vision and Pattern Recognition)
  2. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  3. Jonathan Ho (in Computer Vision and Pattern Recognition)
  4. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  5. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  6. Alexei A. Efros (in Computer Vision and Pattern Recognition)
  7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  8. Jitendra Malik (in Computer Vision and Pattern Recognition)
  9. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  10. Karen Simonyan (in Computer Vision and Pattern Recognition)

Papers from Yue Huang's perspective

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

  1. Attention is All you Need (Neural Information Processing Systems)
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  3. Language Models are Few-Shot Learners (Neural Information Processing Systems)
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. ImageNet classification with deep convolutional neural networks
  6. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
  7. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  8. Improving Language Understanding by Generative Pre-Training
  9. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
  10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers