What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Feature extraction, Feature and Computer vision.
The Artificial intelligence study combines topics in areas such as Frame and Machine learning.
His Pattern recognition research includes themes of Image quality and Fingerprint.
His study in Feature extraction is interdisciplinary in nature, drawing from both Artificial neural network, Facial recognition system and Hidden Markov model.
His Feature study incorporates themes from Image segmentation, Digital image, Representation, Matching and Demosaicing.
His Computer vision research incorporates elements of Distortion and Binary number.
His most cited work include:
- Eigenfeature Regularization and Extraction in Face Recognition (281 citations)
- Errata to “Nonlinear Dynamic System Identification Using Chebyshev Functional Link Artificial Neural Networks” (256 citations)
- Global Context-Aware Attention LSTM Networks for 3D Action Recognition (247 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Algorithm.
Within one scientific family, Alex C. Kot focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Benchmark.
His Pattern recognition study integrates concerns from other disciplines, such as Image processing, Image restoration, Facial recognition system and Fingerprint.
His Computer vision research is multidisciplinary, relying on both Distortion and Binary number.
His biological study spans a wide range of topics, including Visualization, Feature, Discriminative model and Convolutional neural network.
While the research belongs to areas of Algorithm, Alex C. Kot spends his time largely on the problem of Electronic engineering, intersecting his research to questions surrounding Communication channel.
He most often published in these fields:
- Artificial intelligence (65.63%)
- Pattern recognition (32.82%)
- Computer vision (29.72%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (65.63%)
- Pattern recognition (32.82%)
- Machine learning (10.53%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Feature and Feature extraction.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Computer vision.
The various areas that Alex C. Kot examines in his Pattern recognition study include 3D pose estimation and Boosting.
His Machine learning research is multidisciplinary, incorporating perspectives in Adversarial system, Sample and Robustness.
His Feature research includes elements of Motion, Representation, Matching, Feature learning and Generalization.
His Feature extraction study combines topics in areas such as Visualization, Coding, Information retrieval and Hidden Markov model.
Between 2017 and 2021, his most popular works were:
- Domain Generalization with Adversarial Feature Learning (244 citations)
- Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification (234 citations)
- Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (212 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Feature and Benchmark.
His studies deal with areas such as Focus, Machine learning and Generalization as well as Artificial intelligence.
Alex C. Kot combines subjects such as Image and Spoofing attack with his study of Pattern recognition.
In his study, Prior probability and Image processing is strongly linked to Content, which falls under the umbrella field of Image.
His Feature extraction study combines topics from a wide range of disciplines, such as Distributed computing, Artificial neural network, Lossless compression, Visualization and Hidden Markov model.
His Feature research integrates issues from Matching, Regularization, Representation and Training set.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
