Jianfeng Gao
Computer Vision and Pattern Recognition · with Sun · 2024
A researcher in the Science corpus: 1 paper; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 71 citations into it
Whom Jianfeng Gao trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He (in Computer Vision and Pattern Recognition)
- Ross B. Girshick (in Computer Vision and Pattern Recognition)
- Jonathan Ho (in Computer Vision and Pattern Recognition)
- Alex Krizhevsky (in Computer Vision and Pattern Recognition)
- Andrew Zisserman (in Computer Vision and Pattern Recognition)
- Alexei A. Efros (in Computer Vision and Pattern Recognition)
- Shaoqing Ren (in Computer Vision and Pattern Recognition)
- Jitendra Malik (in Computer Vision and Pattern Recognition)
- Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
- Karen Simonyan (in Computer Vision and Pattern Recognition)
Papers from Jianfeng Gao's perspective
Ordered by how strongly the researchers Jianfeng Gao trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
- Improving Language Understanding by Generative Pre-Training
- Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
- AUTO-ENCODING VARIATIONAL BAYES
- Generative Adversarial Networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)