Wonmin Byeon

Computer Vision and Pattern Recognition · 2015–2023

A researcher in the Science corpus: 6 papers; trusts 715 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 226 citations into it; #4,144 of 21,575 by trust here, #4,525 by citations
• Artificial Intelligence — 148 citations into it; #34,666 of 35,345 by trust here, #8,077 by citations
• Generative Models — 45 citations into it
• Model Architectures — 45 citations into it; #1,365 of 2,278 by trust here, #776 by citations
• Signal Processing — 26 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Statistical and Nonlinear Physics — 17 citations into it
• Optimization & Training — 13 citations into it

Whom Wonmin Byeon trusts overall

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

1. Alex Graves
2. J. Urgen Schmidhuber
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Yoshua Bengio
7. Diederik P. Kingma
8. Kaiming He
9. Yann LeCun
10. Ross B. Girshick

Who trusts Wonmin Byeon overall

1. Sifei Liu
2. Francesco Visin
3. Jun Fu
4. Alberto Garcia-Garcia
5. Xiaodan Liang
6. Liang-Chieh Chen
7. Zhihua Liu
8. Sean Bell
9. Bing Shuai
10. Anastasia Ioannidou

Papers from Wonmin Byeon's perspective

Ordered by how strongly the researchers Wonmin Byeon 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. 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

• Scene labeling with LSTM recurrent neural networks (2015) — 365 citations
• GroupViT: Semantic Segmentation Emerges from Text Supervision (2022) — 748 citations
• Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation (2015) — 300 citations
• Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models (2023) — 479 citations
• Sound-Guided Semantic Image Manipulation (2021) — 54 citations
• Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks (2018) — 561 citations