Chee Seng Chan
Computer Vision and Pattern Recognition · 2014–2019
A researcher in the Science corpus: 8 papers; trusts 1,117 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 299 citations into it; #4,486 of 21,575 by trust here, #5,464 by citations
- Artificial Intelligence — 133 citations into it; #8,658 of 35,345 by trust here, #12,580 by citations
- Media Technology — 101 citations into it; #414 of 3,621 by trust here, #494 by citations
- Model Architectures — 28 citations into it
- Atmospheric Science — 27 citations into it
- Biomedical Engineering — 25 citations into it
- Evaluation & Benchmarks — 22 citations into it
- Ecology — 18 citations into it; #4,044 of 10,717 by trust here, #5,577 by citations
Whom Chee Seng Chan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Matthew D. Zeiler
- Diederik P. Kingma
- Andrew Zisserman
Who trusts Chee Seng Chan overall
- Shangbang Long
- Chenyu Gao
- Yuliang Liu
- Pengyuan Lyu
- Andreas Kamilaris
- Jana Wäldchen
- Chee-Kheng Chng
- Cong Yao
- Wenhai Wang
- Chengquan Zhang
Papers from Chee Seng Chan's perspective
Ordered by how strongly the researchers Chee Seng Chan 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- A robust arbitrary text detection system for natural scene images (2014) — 551 citations
- Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition (2017) — 542 citations
- Getting to Know Low-light Images with The Exclusively Dark Dataset (2018) — 833 citations
- Total-Text: toward orientation robustness in scene text detection (2019) — 138 citations
- Fuzzy human motion analysis: A review (2014) — 91 citations
- Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017) — 653 citations
- How deep learning extracts and learns leaf features for plant classification (2017) — 525 citations
- Deep-plant: Plant identification with convolutional neural networks (2015) — 442 citations