Irene Kotsia

Computer Vision and Pattern Recognition · 2006–2020

A researcher in the Science corpus: 9 papers; trusts 635 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 162 citations into it; #9,103 of 21,575 by trust here, #5,987 by citations
• Experimental and Cognitive Psychology — 48 citations into it; #3,326 of 9,731 by trust here, #3,227 by citations
• Signal Processing — 42 citations into it
• Artificial Intelligence — 26 citations into it
• Cognitive Neuroscience — 13 citations into it
• Computational Mechanics — 13 citations into it; #7,448 of 10,001 by trust here, #2,942 by citations
• Model Architectures — 9 citations into it
• Optimization & Training — 6 citations into it

Whom Irene Kotsia trusts overall

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

1. Kaiming He
2. Yuhai Wu
3. Geoffrey E. Hinton
4. Maja Pantic
5. Ross B. Girshick
6. Takeo Kanade
7. Alex Krizhevsky
8. Yann LeCun
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Irene Kotsia overall

1. Vinay Bettadapura
2. Ciprian Adrian Corneanu
3. Pradip Dhal
4. Jeffrey F. Cohn
5. Jiankang Deng
6. Marc Oliu
7. Fernando De la Torre
8. Samuel Albanie
9. Michel François Valstar
10. Carlos Busso

Papers from Irene Kotsia's perspective

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

Their papers

• GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction (2019) — 368 citations
• AgeDB: The First Manually Collected, In-the-Wild Age Database (2017) — 877 citations
• RetinaFace: Single-stage Dense Face Localisation in the Wild (2019) — 747 citations
• RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild (2020) — 442 citations
• Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018) — 518 citations
• Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017) — 408 citations
• Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (2007) — 711 citations
• The eNTERFACE'05 Audio-Visual Emotion Database (2006) — 405 citations
• The eNTERFACE’05 Audio-Visual Emotion Database (2006) — 326 citations