Weiqiang Ren

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 4 papers; trusts 730 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #3,973 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 58 citations into it
• Media Technology — 11 citations into it
• Aerospace Engineering — 10 citations into it; #1,509 of 6,421 by trust here, #2,367 by citations
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it; #2,127 of 3,145 by trust here, #2,691 by citations

Whom Weiqiang Ren trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Kaiming He
4. Geoffrey E. Hinton
5. Andrew Zisserman
6. Yann LeCun
7. Jia Deng
8. Ilya Sutskever
9. Diederik P. Kingma
10. Pedro F. Felzenszwalb

Who trusts Weiqiang Ren overall

1. Fang Wan
2. Krishna Kumar Singh
3. Hakan Bilen
4. Peng Tang
5. Minsu Cho
6. Aditya Arun
7. Yongqiang Zhang
8. Kai Chen
9. Vadim Kantorov
10. Hao Chen

Papers from Weiqiang Ren's perspective

Ordered by how strongly the researchers Weiqiang Ren 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. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Weakly Supervised Object Localization with Latent Category Learning (2014) — 198 citations
• High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection (2019) — 395 citations
• Large-Scale Weakly Supervised Object Localization via Latent Category Learning (2015) — 64 citations
• Focal and Efficient IOU Loss for Accurate Bounding Box Regression (2021) — 1,875 citations