What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Machine learning, Data mining and Information retrieval are his primary areas of study.
His work in Discriminative model, Embedding, Deep learning, Convolutional neural network and Feature vector are all subfields of Artificial intelligence research.
His Pattern recognition study combines topics in areas such as Annotation, Automatic image annotation and Cluster analysis.
His Machine learning study which covers Training set that intersects with Active learning, Preference learning, Recommender system and Probabilistic logic.
His biological study spans a wide range of topics, including Representation and Relevance feedback.
The various areas that Yueting Zhuang examines in his Relevance feedback study include Semantics, Construct, Multimedia and Modality.
His most cited work include:
- Adaptive key frame extraction using unsupervised clustering (472 citations)
- Deeply-Learned Part-Aligned Representations for Person Re-identification (396 citations)
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection (392 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Information retrieval, Pattern recognition, Machine learning and Computer vision.
His Artificial intelligence study frequently draws connections to adjacent fields such as Natural language processing.
Yueting Zhuang has included themes like Image retrieval, Visual Word, Relevance feedback and Semantics in his Information retrieval study.
His Pattern recognition research includes themes of Data mining and Cluster analysis.
His research integrates issues of Embedding and Representation in his study of Machine learning.
His study on Motion capture, Face, Motion estimation and Image is often connected to Face hallucination as part of broader study in Computer vision.
He most often published in these fields:
- Artificial intelligence (56.43%)
- Information retrieval (22.14%)
- Pattern recognition (21.43%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (56.43%)
- Machine learning (18.57%)
- Question answering (7.62%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Question answering, Natural language processing and Representation.
His work is dedicated to discovering how Artificial intelligence, Pattern recognition are connected with Object detection and other disciplines.
His Machine learning study integrates concerns from other disciplines, such as Classifier, Perspective and Code.
The concepts of his Code study are interwoven with issues in Pixel and Discriminative model.
His work carried out in the field of Question answering brings together such families of science as Structure, Relation, Natural language and Transformer.
The study incorporates disciplines such as Image, Closed captioning, Encoder and Benchmark in addition to Representation.
Between 2018 and 2021, his most popular works were:
- Self-Supervised Spatiotemporal Learning via Video Clip Order Prediction (110 citations)
- Counterfactual Samples Synthesizing for Robust Visual Question Answering (34 citations)
- ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Yueting Zhuang mainly investigates Artificial intelligence, Question answering, Machine learning, Pattern recognition and Natural language.
In his research, Yueting Zhuang performs multidisciplinary study on Artificial intelligence and Development plan.
Yueting Zhuang interconnects Image restoration, Iterative reconstruction and Categorization in the investigation of issues within Pattern recognition.
His Natural language research integrates issues from Feature, Frame, Encoder and Modality, Human–computer interaction.
His research in Human–computer interaction intersects with topics in Semantics and Feature extraction.
His studies deal with areas such as Domain and Structure as well as Information retrieval.
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