Jicong Zhang
Computer Vision and Pattern Recognition · 2022
A researcher in the Science corpus: 1 paper; trusts 24 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 61 citations into it
- Artificial Intelligence — 41 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
Whom Jicong Zhang trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas Brox (in Computer Vision and Pattern Recognition)
- Kaiming He (in Computer Vision and Pattern Recognition)
- Ross B. Girshick (in Computer Vision and Pattern Recognition)
- Shaoqing Ren (in Computer Vision and Pattern Recognition)
- Olaf Ronneberger (in Computer Vision and Pattern Recognition)
- Alex Krizhevsky (in Computer Vision and Pattern Recognition)
- Andrew Zisserman (in Computer Vision and Pattern Recognition)
- X. Zhang (in Computer Vision and Pattern Recognition)
- Timo Aila (in Computer Vision and Pattern Recognition)
- Samuli Laine (in Computer Vision and Pattern Recognition)
Papers from Jicong Zhang's perspective
Ordered by how strongly the researchers Jicong Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)