What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Hidden Markov model.
His research on Artificial intelligence often connects related topics like Data mining.
His Machine learning research incorporates elements of RGB color model, Semantics and Robustness.
His biological study spans a wide range of topics, including Matching, Visual search, Digital television and Identification.
His research in Pattern recognition intersects with topics in 3D pose estimation, Pose and Boosting.
His Hidden Markov model study integrates concerns from other disciplines, such as Artificial neural network, Multimedia and Support vector machine.
His most cited work include:
- Global Context-Aware Attention LSTM Networks for 3D Action Recognition (247 citations)
- Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (212 citations)
- A unified framework for semantic shot classification in sports video (195 citations)
What are the main themes of his work throughout his whole career to date?
Ling-Yu Duan focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning.
His study in Discriminative model, Image retrieval, Visualization, Image and Visual search are all subfields of Artificial intelligence.
His Image retrieval research incorporates themes from Query expansion, Information retrieval, Data mining and Quantization.
Ling-Yu Duan focuses mostly in the field of Computer vision, narrowing it down to matters related to Coding and, in some cases, Machine vision.
His Pattern recognition research integrates issues from Feature and Benchmark.
The Multiple kernel learning research Ling-Yu Duan does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Structure and Class, therefore creating a link between diverse domains of science.
He most often published in these fields:
- Artificial intelligence (71.59%)
- Computer vision (31.37%)
- Pattern recognition (29.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (71.59%)
- Computer vision (31.37%)
- Coding (5.90%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Coding, Deep learning and Feature extraction.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His work in Machine learning tackles topics such as Semantics which are related to areas like Parsing and Embedding.
His work on Image, Face and Ground truth as part of general Computer vision study is frequently connected to Reflection and Separation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Codec and Machine vision as well as Coding.
His study in Deep learning is interdisciplinary in nature, drawing from both Vector quantization, Algorithm, Codebook and Quantization.
Between 2019 and 2021, his most popular works were:
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (159 citations)
- Skeleton-Based Online Action Prediction Using Scale Selection Network (53 citations)
- Feature Boosting Network For 3D Pose Estimation (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Ling-Yu Duan mainly focuses on Artificial intelligence, Benchmark, Deep learning, Pattern recognition and Feature extraction.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Coding.
Ling-Yu Duan combines subjects such as Pixel, Computer vision and Multimedia with his study of Coding.
His work carried out in the field of Benchmark brings together such families of science as Algorithm and Representation.
His work investigates the relationship between Deep learning and topics such as Visualization that intersect with problems in Lossless compression, Distributed computing, Lossy compression, Data compression and Analytics.
Ling-Yu Duan has researched Pattern recognition in several fields, including 3D pose estimation, Pose and Boosting.
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