Qingyao Wu

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 1,040 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 313 citations into it; #10,055 of 21,575 by trust here, #4,174 by citations
• Artificial Intelligence — 256 citations into it; #20,039 of 35,345 by trust here, #6,103 by citations
• Model Architectures — 60 citations into it
• Generative Models — 57 citations into it
• Representation & Self-Supervised Learning — 55 citations into it
• Natural Language Processing — 45 citations into it
• Optimization & Training — 42 citations into it
• Efficiency & Compression — 34 citations into it

Whom Qingyao Wu trusts overall

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

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

Who trusts Qingyao Wu overall

1. Yong Guo
2. Jing Liu
3. Jian Chen
4. Mingkui Tan
5. Chi Zhang

Papers from Qingyao Wu's perspective

Ordered by how strongly the researchers Qingyao Wu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation (2019) — 358 citations
• Discrimination-aware Channel Pruning for Deep Neural Networks (2018) — 637 citations
• Visual Grounding Via Accumulated Attention (2020) — 205 citations
• Adversarial Learning with Local Coordinate Coding (2018) — 39 citations
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (2019) — 188 citations
• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019) — 70 citations
• Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis (2019) — 69 citations
• Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks (2019) — 55 citations
• Double Forward Propagation for Memorized Batch Normalization (2018) — 29 citations
• A Unified Framework for Metric Transfer Learning (2017) — 171 citations
• The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs (2016) — 13 citations