Beomyoung Kim

Computer Vision and Pattern Recognition · 2021–2024

A researcher in the Science corpus: 3 papers; trusts 726 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it
• Artificial Intelligence — 61 citations into it
• Meta & Continual Learning — 23 citations into it
• Model Architectures — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Beomyoung Kim trusts overall

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

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

Who trusts Beomyoung Kim overall

1. Huyong Wang
2. Youngmin Oh
3. Zekang Zhang
4. Liyuan Wang
5. Lanyun Zhu
6. Jinpeng Chen

Papers from Beomyoung Kim's perspective

Ordered by how strongly the researchers Beomyoung Kim 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning (2024) — 33 citations
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (2021) — 135 citations
• Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (2021) — 138 citations