Jungseock Joo

Computer Vision and Pattern Recognition · 2013–2021

A researcher in the Science corpus: 7 papers; trusts 952 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 138 citations into it; #7,527 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 53 citations into it; #29,433 of 35,345 by trust here, #32,362 by citations
• Signal Processing — 33 citations into it; #3,155 of 7,572 by trust here, #3,099 by citations
• Safety, Robustness & Interpretability — 32 citations into it
• Evaluation & Benchmarks — 25 citations into it; #1,459 of 3,147 by trust here, #2,704 by citations
• Sociology and Political Science — 21 citations into it; #14,341 of 22,756 by trust here, #9,515 by citations
• Generative Models — 14 citations into it
• Cognitive Neuroscience — 11 citations into it

Whom Jungseock Joo trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Thorsten Joachims
5. Alex Krizhevsky
6. Yoshua Bengio
7. Andrew Zisserman
8. Navneet Dalal
9. Yann LeCun
10. Ross B. Girshick

Who trusts Jungseock Joo overall

1. Kimmo Kärkkäinen
2. Seyoung Park
3. Mingda Zhang
4. Song-Chun Zhu
5. Ning Zhang
6. Adriana Kovashka
7. Zaid Khan
8. Christopher Thomas
9. Francis F. Steen
10. Donghyeon Won

Papers from Jungseock Joo's perspective

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

Their papers

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021) — 785 citations
• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019) — 206 citations
• Human Attribute Recognition by Rich Appearance Dictionary (2013) — 43 citations
• Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face (2015) — 72 citations
• Visual Persuasion: Inferring Communicative Intents of Images (2014) — 140 citations
• Protest Activity Detection and Perceived Violence Estimation from Social Media Images (2017) — 104 citations
• Understanding the Political Ideology of Legislators from Social Media Images (2019) — 54 citations