Jonghwan Mun

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 948 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #13,991 of 21,575 by trust here, #9,435 by citations
• Artificial Intelligence — 113 citations into it; #27,182 of 35,345 by trust here, #24,417 by citations
• Model Architectures — 32 citations into it
• Multimodal Learning — 25 citations into it
• Natural Language Processing — 20 citations into it
• Efficiency & Compression — 15 citations into it
• Optimization & Training — 14 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Jonghwan Mun trusts overall

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

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

Who trusts Jonghwan Mun overall

1. Paul Hongsuck Seo
2. Jie Lei
3. Minsoo Kang
4. Yuang Liu
5. Hao Zhou
6. Jianping Gou
7. Bohyung Han
8. Yunseok Jang
9. Santiago Castro
10. Seongho Choi

Papers from Jonghwan Mun's perspective

Ordered by how strongly the researchers Jonghwan Mun 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MarioQA: Answering Questions by Watching Gameplay Videos (2016) — 114 citations
• Local-Global Video-Text Interactions for Temporal Grounding (2020) — 354 citations
• Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization (2017) — 233 citations
• Towards Oracle Knowledge Distillation with Neural Architecture Search (2019) — 49 citations
• Learning to Specialize with Knowledge Distillation for Visual Question Answering (2018) — 39 citations