Seokeon Choi
Computer Vision and Pattern Recognition · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 322 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 174 citations into it
- Artificial Intelligence — 39 citations into it
- Generative Models — 12 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
- Media Technology — 6 citations into it
Whom Seokeon Choi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Navneet Dalal
- Mang Ye
- Karen Simonyan
- Yoshua Bengio
- Jia Deng
Who trusts Seokeon Choi overall
Papers from Seokeon Choi's perspective
Ordered by how strongly the researchers Seokeon Choi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
- The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
- Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection (2019) — 338 citations
- Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification (2019) — 311 citations