Chris Buehler

Computer Vision and Pattern Recognition · 2000–2017

A researcher in the Science corpus: 5 papers; trusts 312 researchers and is trusted by 942 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 86 citations into it; #1,400 of 21,575 by trust here, #1,075 by citations
• Artificial Intelligence — 63 citations into it; #2,882 of 35,345 by trust here, #2,121 by citations
• Multimodal Learning — 45 citations into it; #654 of 1,211 by trust here, #575 by citations
• Natural Language Processing — 32 citations into it; #920 of 5,688 by trust here, #503 by citations
• Evaluation & Benchmarks — 17 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 6 citations into it
• Computer Graphics and Computer-Aided Design — 5 citations into it; #358 of 1,584 by trust here, #210 by citations

Whom Chris Buehler trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Matthew D. Zeiler
6. Ross B. Girshick
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Sepp Hochreiter

Who trusts Chris Buehler overall

1. Zhe Gan
2. Peter Hedman
3. Ronghang Hu
4. Linjie Li
5. Christian Reiser
6. Joel Carranza
7. Wojciech Matusik
8. Hanwang Zhang
9. Noah Snavely
10. Mohit Bansal

Papers from Chris Buehler's perspective

Ordered by how strongly the researchers Chris Buehler 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering (2017) — 4,718 citations
• Bottom-Up and Top-Down Attention for Image Captioning and VQA (2017) — 357 citations
• Unstructured lumigraph rendering (2001) — 1,074 citations
• Image-based visual hulls (2000) — 1,086 citations
• Polyhedral Visual Hulls for Real-Time Rendering (2001) — 341 citations