Seunghoon Hong

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 9 papers; trusts 1,413 researchers and is trusted by 3,371 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 250 citations into it; #616 of 21,575 by trust here, #879 by citations
• Artificial Intelligence — 94 citations into it; #7,998 of 35,345 by trust here, #8,007 by citations
• Generative Models — 44 citations into it; #653 of 1,956 by trust here, #499 by citations
• Evaluation & Benchmarks — 31 citations into it
• Model Architectures — 29 citations into it
• Media Technology — 18 citations into it
• Aerospace Engineering — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Seunghoon Hong trusts overall

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

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

Who trusts Seunghoon Hong overall

1. Liang-Chieh Chen
2. Tianfu Wu
3. Jiwoon Ahn
4. Chunhua Shen
5. Bohyung Han
6. Andrea Vedaldi
7. Hyeonseob Nam
8. Trevor Darrell
9. Ming-Hsuan Yang
10. Wanli Ouyang

Papers from Seunghoon Hong's perspective

Ordered by how strongly the researchers Seunghoon Hong 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 Deconvolution Network for Semantic Segmentation (2015) — 4,362 citations
• Decomposing Motion and Content for Natural Video Sequence Prediction (2017) — 639 citations
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015) — 345 citations
• The Visual Object Tracking VOT2014 Challenge Results (2014) — 512 citations
• Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis (2018) — 359 citations
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network (2015) — 177 citations
• Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (2015) — 852 citations
• Weakly Supervised Semantic Segmentation Using Web-Crawled Videos (2017) — 161 citations
• Diversity-Sensitive Conditional Generative Adversarial Networks (2019) — 223 citations