What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
Wanqing Li mainly focuses on Artificial intelligence, Pattern recognition, Discriminative model, Computer vision and Convolutional neural network.
His Feature extraction, Artificial neural network and Unsupervised learning study in the realm of Artificial intelligence connects with subjects such as Action recognition.
His Pattern recognition research focuses on subjects like Joint, which are linked to Linear subspace.
His Discriminative model research includes elements of Neocognitron and Gesture.
His work deals with themes such as Graph theory, Graph and Hidden Markov model, which intersect with Computer vision.
His research investigates the connection between Convolutional neural network and topics such as RGB color model that intersect with problems in Deep learning, Color space and Kernel.
His most cited work include:
- Action recognition based on a bag of 3D points (1050 citations)
- The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans. (856 citations)
- Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks (250 citations)
What are the main themes of his work throughout his whole career to date?
Wanqing Li spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Convolutional neural network.
Wanqing Li undertakes multidisciplinary investigations into Artificial intelligence and Action recognition in his work.
His work in Pattern recognition covers topics such as Object detection which are related to areas like Template matching.
His Computer graphics research extends to Computer vision, which is thematically connected.
His studies in Convolutional neural network integrate themes in fields like Recurrent neural network, Deep learning and Pooling.
The RGB color model study combines topics in areas such as Modality and Skeleton.
He most often published in these fields:
- Artificial intelligence (72.89%)
- Pattern recognition (46.52%)
- Computer vision (34.43%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (72.89%)
- Pattern recognition (46.52%)
- Convolutional neural network (13.55%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Convolutional neural network, RGB color model and Feature extraction.
As a part of the same scientific family, Wanqing Li mostly works in the field of Artificial intelligence, focusing on Computer vision and, on occasion, Neocognitron.
His work on Discriminative model as part of his general Pattern recognition study is frequently connected to Scale, thereby bridging the divide between different branches of science.
His research in Convolutional neural network intersects with topics in Representation, Pooling and Encoding.
His RGB color model research is multidisciplinary, incorporating perspectives in Modality and Skeleton.
Wanqing Li has included themes like Convolution and Feature in his Feature extraction study.
Between 2016 and 2021, his most popular works were:
- Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN (213 citations)
- Joint Geometrical and Statistical Alignment for Visual Domain Adaptation (195 citations)
- Importance Weighted Adversarial Nets for Partial Domain Adaptation (173 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
Wanqing Li mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, RGB color model and Feature extraction.
In his study, Wanqing Li carries out multidisciplinary Artificial intelligence and Construct research.
His Pattern recognition research incorporates themes from Kernel, Computer vision, Robustness and Joint.
His research investigates the connection with Computer vision and areas like Neocognitron which intersect with concerns in Segmentation.
His RGB color model study combines topics from a wide range of disciplines, such as Modality and Deep learning.
The various areas that Wanqing Li examines in his Feature extraction study include Sparse approximation and Gesture, Gesture recognition.
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