Zifeng Wu
Computer Vision and Pattern Recognition · 2014–2019
A researcher in the Science corpus: 7 papers; trusts 1,078 researchers and is trusted by 400 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 202 citations into it; #2,090 of 21,575 by trust here, #2,949 by citations
- Artificial Intelligence — 76 citations into it; #4,896 of 35,345 by trust here, #5,533 by citations
- Model Architectures — 46 citations into it
- Evaluation & Benchmarks — 29 citations into it
- Media Technology — 26 citations into it; #2,684 of 3,621 by trust here, #2,389 by citations
- Aerospace Engineering — 19 citations into it
- Biomedical Engineering — 16 citations into it
- Optimization & Training — 16 citations into it
Whom Zifeng Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Ross B. Girshick
- Kaiming He
- Geoffrey E. Hinton
- Karen Simonyan
- Yann LeCun
- Ilya Sutskever
- Christian Szegedy
- Andrew Zisserman
- Yoshua Bengio
Who trusts Zifeng Wu overall
- Paul Voigtlaender
- Liang-Chieh Chen
- Hengshuang Zhao
- Bastian Leibe
- Chunhua Shen
- Yuanzhouhan Cao
- Mennatullah Siam
- Samuel Rota Bulò
- Changqian Yu
- Huiyu Wang
Papers from Zifeng Wu's perspective
Ordered by how strongly the researchers Zifeng Wu 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
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- 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
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition (2016) — 1,731 citations
- Bridging Category-level and Instance-level Semantic Image Segmentation (2016) — 176 citations
- High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks (2016) — 130 citations
- Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks (2016) — 454 citations
- A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs (2017) — 642 citations
- REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019) — 863 citations
- Feature Coding in Image Classification: A Comprehensive Study (2014) — 268 citations