Jie Zhou
Computer Vision and Pattern Recognition · with Chen · 2022–2023
A researcher in the Science corpus: 2 papers; trusts 42 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 81 citations into it
- Artificial Intelligence — 55 citations into it
Whom Jie Zhou trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He (in Computer Vision and Pattern Recognition)
- X. Zhang (in Computer Vision and Pattern Recognition)
- Shaoqing Ren (in Computer Vision and Pattern Recognition)
- Ross B. Girshick (in Computer Vision and Pattern Recognition)
- Trevor Darrell (in Computer Vision and Pattern Recognition)
- Andrew Zisserman (in Computer Vision and Pattern Recognition)
- Alex Krizhevsky (in Computer Vision and Pattern Recognition)
- Piotr Doll'ar (in Computer Vision and Pattern Recognition)
- Jitendra Malik (in Computer Vision and Pattern Recognition)
- Li Fei-Fei (in Computer Vision and Pattern Recognition)
Papers from Jie Zhou's perspective
Ordered by how strongly the researchers Jie Zhou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- SGDR: Stochastic Gradient Descent with Warm Restarts
- End-to-End Object Detection with Transformers (European Conference on Computer Vision)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)