Eunbyung Park
Computer Vision and Pattern Recognition · with Berg · 2015–2019
A researcher in the Science corpus: 8 papers; trusts 1,352 researchers and is trusted by 448 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 235 citations into it; #1,993 of 21,575 by trust here, #3,909 by citations
- Artificial Intelligence — 116 citations into it; #10,392 of 35,345 by trust here, #9,092 by citations
- Natural Language Processing — 32 citations into it
- Optimization & Training — 30 citations into it; #1,299 of 2,601 by trust here, #1,866 by citations
- Representation & Self-Supervised Learning — 30 citations into it
- Evaluation & Benchmarks — 24 citations into it
- Model Architectures — 24 citations into it
- Multimodal Learning — 23 citations into it; #402 of 1,211 by trust here, #569 by citations
Whom Eunbyung Park trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Karen Simonyan
- Diederik P. Kingma
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Andrew Zisserman
Who trusts Eunbyung Park overall
- Mengye Ren
- Zhipeng Zhang
- Guha Balakrishnan
- Sebastian Flennerhag
- Christian Simon
- Bert De Brabandere
- Luc Van Gool
- Siyuan Shan
- Sébastien M. R. Arnold
- Johannes von Oswald
Papers from Eunbyung Park's perspective
Ordered by how strongly the researchers Eunbyung Park trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Visual Madlibs: Fill in the blank Image Generation and Question Answering (2015) — 98 citations
- Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017) — 294 citations
- Visual Madlibs: Fill in the Blank Description Generation and Question Answering (2015) — 146 citations
- A dataset for developing and benchmarking active vision (2017) — 209 citations
- Learning to decompose for object detection and instance segmentation (2015) — 25 citations
- Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers (2018) — 175 citations
- Combining multiple sources of knowledge in deep CNNs for action recognition (2016) — 185 citations
- Meta-Curvature (2019) — 142 citations