Qilong Wang

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 971 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 192 citations into it; #8,420 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 110 citations into it; #32,510 of 35,345 by trust here, #7,471 by citations
• Model Architectures — 49 citations into it
• Media Technology — 30 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 15 citations into it
• Computational Mechanics — 14 citations into it; #8,822 of 10,001 by trust here, #6,877 by citations
• Evaluation & Benchmarks — 13 citations into it

Whom Qilong Wang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Christian Szegedy
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. X. Zhang

Who trusts Qilong Wang overall

1. Meng-Hao Guo
2. Zequn Qin
3. Qing-Long Zhang
4. Chun-Fu Chen
5. Shunyi Zheng
6. Chao Li
7. Jungkyu Lee
8. Haonan Wang
9. Heliang Zheng
10. Swalpa Kumar Roy

Papers from Qilong Wang's perspective

Ordered by how strongly the researchers Qilong Wang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (2017) — 650 citations
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (2019) — 6,251 citations
• Multi-scale Location-Aware Kernel Representation for Object Detection (2018) — 71 citations
• Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization (2017) — 305 citations
• Is Second-Order Information Helpful for Large-Scale Visual Recognition? (2017) — 280 citations
• Neural Blind Deconvolution Using Deep Priors (2019) — 332 citations
• Global Second-Order Pooling Convolutional Networks (2018) — 413 citations