Douglas Summers-Stay

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 3 papers; trusts 393 researchers and is trusted by 848 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it; #1,486 of 21,575 by trust here, #1,203 by citations
• Artificial Intelligence — 97 citations into it; #1,754 of 35,345 by trust here, #1,605 by citations
• Multimodal Learning — 55 citations into it; #108 of 1,211 by trust here, #114 by citations
• Natural Language Processing — 45 citations into it; #2,805 of 5,688 by trust here, #2,220 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 6 citations into it

Whom Douglas Summers-Stay trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Karen Simonyan
7. Ilya Sutskever
8. Yann LeCun
9. Ross B. Girshick
10. Andrew Zisserman

Who trusts Douglas Summers-Stay overall

1. Ronghang Hu
2. Damien Teney
3. Drew A. Hudson
4. Kushal Kafle
5. Jaemin Cho
6. Zhe Gan
7. Mohit Bansal
8. Linjie Li
9. Zhou Yu
10. Anton van den Hengel

Papers from Douglas Summers-Stay's perspective

Ordered by how strongly the researchers Douglas Summers-Stay trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering (2016) — 4,360 citations
• Yin and Yang: Balancing and Answering Binary Visual Questions (2015) — 373 citations
• What Can a Generative Language Model Answer About a Passage? (2021)