Shipeng Yan
Computer Vision and Pattern Recognition · 2018–2022
A researcher in the Science corpus: 9 papers; trusts 1,232 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 258 citations into it; #4,299 of 21,575 by trust here, #4,690 by citations
- Artificial Intelligence — 204 citations into it; #4,843 of 35,345 by trust here, #7,346 by citations
- Representation & Self-Supervised Learning — 74 citations into it; #692 of 3,145 by trust here, #1,204 by citations
- Meta & Continual Learning — 70 citations into it; #113 of 1,041 by trust here, #218 by citations
- Model Architectures — 37 citations into it
- Radiology, Nuclear Medicine and Imaging — 34 citations into it
- Optimization & Training — 28 citations into it
- Evaluation & Benchmarks — 23 citations into it
Whom Shipeng Yan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Ilya Sutskever
- Karen Simonyan
- Shaoqing Ren
Who trusts Shipeng Yan overall
- Da-Wei Zhou
- Gyuhak Kim
- Liyuan Wang
- Han-Jia Ye
- Xialei Liu
- Fu-Yun Wang
- Chang-Bin Zhang
- Jianqiang Xiao
- Marco Toldo
- De-chuan Zhan
Papers from Shipeng Yan's perspective
Ordered by how strongly the researchers Shipeng Yan 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- DER: Dynamically Expandable Representation for Class Incremental Learning (2021) — 671 citations
- FOTS: Fast Oriented Text Spotting with a Unified Network (2018) — 531 citations
- Distribution Alignment: A Unified Framework for Long-tail Visual Recognition (2021) — 365 citations
- How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021) — 39 citations
- An EM Framework for Online Incremental Learning of Semantic Segmentation (2021) — 45 citations
- Generative Negative Text Replay for Continual Vision-Language Pretraining (2022) — 30 citations
- General Incremental Learning with Domain-aware Categorical Representations (2022) — 50 citations
- LatentGNN: Learning Efficient Non-local Relations for Visual Recognition (2019) — 87 citations
- The Rise of Socially Responsible Investment Funds: The Paradoxical Role of the Financial Logic (2019) — 110 citations