Weifeng Ge

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 797 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it; #10,341 of 21,575 by trust here, #9,435 by citations
• Artificial Intelligence — 127 citations into it; #16,260 of 35,345 by trust here, #13,760 by citations
• Signal Processing — 36 citations into it
• Model Architectures — 23 citations into it
• Cognitive Neuroscience — 22 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Biomedical Engineering — 9 citations into it

Whom Weifeng Ge trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ross B. Girshick
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Christian Szegedy
6. Yoshua Bengio
7. Sergey Ioffe
8. Yann LeCun
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Weifeng Ge overall

1. Mahmut Kaya
2. Chris Dongjoo Kim
3. Hao Yang
4. Timo Milbich
5. Karsten Roth
6. Luis Villaseñor Pineda
7. Kevin Musgrave
8. Huiyu Wang
9. Kemal Oksuz
10. Peng Tang

Papers from Weifeng Ge's perspective

Ordered by how strongly the researchers Weifeng Ge trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Metric Learning with Hierarchical Triplet Loss (2018) — 465 citations
• Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning (2018) — 196 citations
• Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up (2019) — 296 citations
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022) — 552 citations