C.-C. Jay Kuo

Computer Vision and Pattern Recognition · 1998–2019

A researcher in the Science corpus: 24 papers; trusts 1,345 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 350 citations into it; #5,949 of 21,575 by trust here, #2,739 by citations
• Artificial Intelligence — 155 citations into it; #17,215 of 35,345 by trust here, #8,525 by citations
• Generative Models — 82 citations into it
• Media Technology — 63 citations into it; #1,082 of 3,621 by trust here, #515 by citations
• Evaluation & Benchmarks — 53 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Natural Language Processing — 41 citations into it
• Model Architectures — 31 citations into it

Whom C.-C. Jay Kuo trusts overall

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

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

Who trusts C.-C. Jay Kuo overall

1. Daniele Barchiesi
2. George Tzanetakis
3. Min Zhang
4. Yanjiao Chen
5. Jingxian Wu
6. Gang Yang
7. Jiali Duan
8. Billy Pik Lik Lau
9. Dan Stowell
10. Dimitrios Giannoulis

Papers from C.-C. Jay Kuo's perspective

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

Their papers

• Instance Embedding Transfer to Unsupervised Video Object Segmentation (2018) — 106 citations
• Class-incremental Learning via Deep Model Consolidation (2019) — 390 citations
• A Fully Convolutional Tri-Branch Network (FCTN) for Domain Adaptation (2017) — 25 citations
• SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting (2018) — 197 citations
• Contextual-Based Image Inpainting: Infer, Match, and Translate (2017) — 227 citations
• MCL-JCV: A JND-based H.264/AVC video quality assessment dataset (2016) — 398 citations
• Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (2017) — 385 citations
• Image database TID 2013 : Peculiarities , results and perspectives (2016) — 1,275 citations
• Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart (2018) — 25 citations
• Image database TID2013: Peculiarities, results and perspectives (2015)
• Synchronization Techniques for Orthogonal Frequency Division Multiple Access (OFDMA): A Tutorial Review (2007) — 772 citations
• Cooperative Communications in Resource-Constrained Wireless Networks (2007) — 463 citations
• Cooperative Communications and Networking (2009) — 776 citations
• Evaluating word embedding models: methods and experimental results (2019) — 308 citations
• An Interpretable Generative Model for Handwritten Digits Synthesis (2019) — 4 citations
• Interpretable Convolutional Neural Networks via Feedforward Design (2018) — 177 citations
• Understanding convolutional neural networks with a mathematical model (2016) — 405 citations
• Color image database TID2013: Peculiarities and preliminary results (2013) — 568 citations
• Perceptual visual quality metrics: A survey (2011) — 968 citations
• Environmental Sound Recognition With Time–Frequency Audio Features (2009) — 707 citations