Hyeonseob Nam

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 8 papers; trusts 1,401 researchers and is trusted by 1,068 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 299 citations into it; #1,293 of 21,575 by trust here, #1,821 by citations
• Artificial Intelligence — 147 citations into it; #6,050 of 35,345 by trust here, #7,010 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Multimodal Learning — 23 citations into it; #245 of 1,211 by trust here, #499 by citations
• Evaluation & Benchmarks — 22 citations into it
• Aerospace Engineering — 21 citations into it
• Model Architectures — 19 citations into it; #1,364 of 2,278 by trust here, #1,940 by citations
• Natural Language Processing — 17 citations into it

Whom Hyeonseob Nam trusts overall

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

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

Who trusts Hyeonseob Nam overall

1. Martin Danelljan
2. Goutam Bhat
3. Ning Wang
4. Tianyu Yang
5. Heng Fan
6. Zhipeng Zhang
7. Luca Bertinetto
8. Anna Khoreva
9. Kurt Shuster
10. Bo Li

Papers from Hyeonseob Nam's perspective

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

• Learning Multi-domain Convolutional Neural Networks for Visual Tracking (2015) — 2,617 citations
• The Visual Object Tracking VOT2014 Challenge Results (2014) — 512 citations
• Dual Attention Networks for Multimodal Reasoning and Matching (2016) — 716 citations
• The Visual Object Tracking VOT2016 Challenge Results (2016) — 773 citations
• Modeling and Propagating CNNs in a Tree Structure for Visual Tracking (2016) — 332 citations
• SRM: A Style-Based Recalibration Module for Convolutional Neural Networks (2019) — 294 citations
• Reducing Domain Gap by Reducing Style Bias (2021) — 471 citations
• Reducing Domain Gap via Style-Agnostic Networks (2019) — 66 citations