Jonathan Munro

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 1,186 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 480 citations into it; #4,913 of 21,575 by trust here, #3,141 by citations
• Artificial Intelligence — 244 citations into it; #23,367 of 35,345 by trust here, #27,551 by citations
• Evaluation & Benchmarks — 110 citations into it; #1,458 of 3,147 by trust here, #822 by citations
• Representation & Self-Supervised Learning — 84 citations into it; #2,297 of 3,145 by trust here, #2,805 by citations
• Multimodal Learning — 69 citations into it; #764 of 1,211 by trust here, #1,119 by citations
• Signal Processing — 64 citations into it
• Human-Computer Interaction — 39 citations into it
• Model Architectures — 33 citations into it

Whom Jonathan Munro trusts overall

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

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

Who trusts Jonathan Munro overall

1. Michael James Horry
2. Mirco Planamente
3. Dima Damen
4. Subrata Chakraborty
5. Yi Cheng
6. Chiara Plizzari
7. Antonino Furnari
8. Hazel Doughty
9. Manoranjan Paul
10. Giovanni Maria Farinella

Papers from Jonathan Munro's perspective

Ordered by how strongly the researchers Jonathan Munro 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. 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

• Scaling Egocentric Vision: The EPIC-KITCHENS Dataset (2018) — 1,366 citations
• Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021) — 1,925 citations
• The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines (2020) — 366 citations
• Scaling Egocentric Vision: The Dataset (2018) — 72 citations
• Multi-Modal Domain Adaptation for Fine-Grained Action Recognition (2020) — 238 citations
• Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020) — 719 citations
• Multi-Modal Domain Adaptation for Fine-Grained Action Recognition (2019) — 85 citations