Dawei Li
Computer Vision and Pattern Recognition · with Zhang · 2016–2020
A researcher in the Science corpus: 5 papers; trusts 504 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 132 citations into it; #11,709 of 21,575 by trust here, #5,236 by citations
- Artificial Intelligence — 96 citations into it; #18,662 of 35,345 by trust here, #7,945 by citations
- Representation & Self-Supervised Learning — 35 citations into it
- Evaluation & Benchmarks — 22 citations into it
- Model Architectures — 21 citations into it
- Optimization & Training — 17 citations into it
- Meta & Continual Learning — 16 citations into it; #987 of 1,041 by trust here, #291 by citations
- Efficiency & Compression — 15 citations into it
Whom Dawei Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Diederik P. Kingma
- Tsung-Yi Lin
- Ilya Sutskever
- Shaoqing Ren
- Yann LeCun
- Jia Deng
Who trusts Dawei Li overall
- Can Peng
- Liyuan Wang
- K. J. Joseph
- Umberto Michieli
- Kun Zhao
- Da-Wei Zhou
- Zheda Mai
- Liyang Liu
- Yaoyao Liu
- Lorenzo Pellegrini
Papers from Dawei Li's perspective
Ordered by how strongly the researchers Dawei Li 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Class-incremental Learning via Deep Model Consolidation (2019) — 390 citations
- RILOD: near real-time incremental learning for object detection at the edge (2019) — 114 citations
- Regularize, Expand and Compress: NonExpansive Continual Learning (2020) — 27 citations
- Efficient Incremental Learning for Mobile Object Detection (2019) — 11 citations
- DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition (2016) — 92 citations