Shaojie Shen
Computer Vision and Pattern Recognition · with Hu · 2024
A researcher in the Science corpus: 2 papers; trusts 97 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 117 citations into it
- Generative Models — 10 citations into it
- Statistical and Nonlinear Physics — 6 citations into it
Whom Shaojie Shen trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Rob Fergus (in Computer Vision and Pattern Recognition)
- Ross B. Girshick (in Computer Vision and Pattern Recognition)
- Kaiming He (in Computer Vision and Pattern Recognition)
- Jitendra Malik (in Computer Vision and Pattern Recognition)
- Andrew Zisserman (in Computer Vision and Pattern Recognition)
- Jia Deng (in Computer Vision and Pattern Recognition)
- Alex Krizhevsky (in Computer Vision and Pattern Recognition)
- Shaoqing Ren (in Computer Vision and Pattern Recognition)
- Trevor Darrell (in Computer Vision and Pattern Recognition)
- Karen Simonyan (in Computer Vision and Pattern Recognition)
Papers from Shaojie Shen's perspective
Ordered by how strongly the researchers Shaojie Shen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)