Dong Huk Park

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 739 researchers and is trusted by 523 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 209 citations into it; #1,860 of 21,575 by trust here, #1,609 by citations
• Artificial Intelligence — 125 citations into it; #3,796 of 35,345 by trust here, #2,425 by citations
• Multimodal Learning — 69 citations into it; #154 of 1,211 by trust here, #166 by citations
• Generative Models — 67 citations into it
• Natural Language Processing — 42 citations into it
• Evaluation & Benchmarks — 32 citations into it; #2,571 of 3,147 by trust here, #2,482 by citations
• Model Architectures — 18 citations into it
• Safety, Robustness & Interpretability — 17 citations into it

Whom Dong Huk Park trusts overall

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

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

Who trusts Dong Huk Park overall

1. Kushal Kafle
2. Oana-Maria Camburu
3. Ronghang Hu
4. Trevor Darrell
5. Zhou Yu
6. Zhe Gan
7. Zeynep Akata
8. Raymond J. Mooney
9. Badri N. Patro
10. Jialin Wu

Papers from Dong Huk Park's perspective

Ordered by how strongly the researchers Dong Huk Park 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding (2016) — 1,594 citations
• Multimodal Explanations: Justifying Decisions and Pointing to the Evidence (2018) — 482 citations
• Toward Transformer-Based Object Detection (2020) — 257 citations
• Attentive Explanations: Justifying Decisions and Pointing to the Evidence (2016) — 80 citations
• More Control for Free! Image Synthesis with Semantic Diffusion Guidance (2021) — 339 citations
• Benchmark for Compositional Text-to-Image Synthesis (2021) — 165 citations