Gaoang Wang

Computer Vision and Pattern Recognition · 2023

A researcher in the Science corpus: 1 paper; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Gaoang Wang trusts

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

  1. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  2. Kaiming He (in Computer Vision and Pattern Recognition)
  3. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  4. Cordelia Schmid (in Computer Vision and Pattern Recognition)
  5. Jitendra Malik (in Computer Vision and Pattern Recognition)
  6. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  8. Karen Simonyan (in Computer Vision and Pattern Recognition)
  9. Li Fei-Fei (in Computer Vision and Pattern Recognition)
  10. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)

Papers from Gaoang Wang's perspective

Ordered by how strongly the researchers Gaoang Wang 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. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
  4. Flamingo: a Visual Language Model for Few-Shot Learning (Neural Information Processing Systems)
  5. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
  6. HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
  7. GPT-4 Technical Report
  8. Visual Instruction Tuning (Neural Information Processing Systems)
  9. VideoBERT: A Joint Model for Video and Language Representation Learning
  10. MSR-VTT: A Large Video Description Dataset for Bridging Video and Language

Their papers