Seonguk Seo

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 492 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 91 citations into it
• Artificial Intelligence — 86 citations into it
• Representation & Self-Supervised Learning — 36 citations into it
• Optimization & Training — 17 citations into it
• Model Architectures — 12 citations into it
• Multimodal Learning — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Seonguk Seo trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Sergey Ioffe
6. Christian Szegedy
7. Diederik P. Kingma
8. Yoshua Bengio
9. Yann LeCun
10. Ross B. Girshick

Who trusts Seonguk Seo overall

1. Kaiyang Zhou
2. Fabian Küppers
3. Jiannan Wu
4. Jan Kronenberger
5. Yi Jiang
6. Ziwei Liu

Papers from Seonguk Seo's perspective

Ordered by how strongly the researchers Seonguk Seo 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Domain-Specific Batch Normalization for Unsupervised Domain Adaptation (2019) — 435 citations
• Learning to Optimize Domain Specific Normalization for Domain Generalization (2019) — 267 citations
• URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark (2020) — 311 citations
• Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences (2018) — 81 citations
• Metric Compatible Training for Online Backfilling in Large-Scale Retrieval (2023) — 9 citations