What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Cha Zhang mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Face detection and Speech recognition.
His research ties Machine learning and Artificial intelligence together.
His work on Image-based modeling and rendering, Rendering and Image sensor as part of his general Computer vision study is frequently connected to Plane, thereby bridging the divide between different branches of science.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Discrete cosine transform and Lapped transform.
His Face detection research is multidisciplinary, relying on both Convolutional neural network, Detector and False positive rate.
The various areas that he examines in his Speech recognition study include Acoustic source localization, Feature and Reverberation.
His most cited work include:
- Multiple Instance Boosting for Object Detection (664 citations)
- A Survey of Recent Advances in Face Detection (435 citations)
- Image based Static Facial Expression Recognition with Multiple Deep Network Learning (349 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Computer graphics, Rendering and Pattern recognition.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Robustness and other disciplines.
His research on Computer graphics also deals with topics like
- Bitstream which intersects with area such as Cache,
- Parallax which connect with Stereopsis.
His Rendering research integrates issues from Acoustics, Loudspeaker and Codec.
His work focuses on many connections between Pattern recognition and other disciplines, such as Boosting, that overlap with his field of interest in Speech recognition.
His work carried out in the field of Face brings together such families of science as Object, Probabilistic logic, Convolutional neural network and Detector.
He most often published in these fields:
- Artificial intelligence (64.91%)
- Computer vision (45.61%)
- Computer graphics (19.30%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (64.91%)
- Machine learning (8.19%)
- Pattern recognition (13.45%)
In recent papers he was focusing on the following fields of study:
Cha Zhang mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Convolutional neural network and Algorithm.
His research on Artificial intelligence often connects related areas such as Computer vision.
His studies in Machine learning integrate themes in fields like Robustness and Code.
In the subject of general Pattern recognition, his work in Classifier is often linked to Minimum mean square error, Smoothness and Maximum a posteriori estimation, thereby combining diverse domains of study.
His Convolutional neural network research incorporates elements of Speech recognition and Emotion classification.
His Algorithm research includes elements of Upsampling, Resource constrained, Artificial neural network, Pruning and Ranking.
Between 2014 and 2021, his most popular works were:
- Image based Static Facial Expression Recognition with Multiple Deep Network Learning (349 citations)
- Automatic speech emotion recognition using recurrent neural networks with local attention (252 citations)
- Training deep networks for facial expression recognition with crowd-sourced label distribution (185 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Convolutional neural network, Artificial intelligence, Algorithm, Speech recognition and Pattern recognition.
His Artificial intelligence research focuses on Deep learning, Regularization and Transform coding.
His biological study spans a wide range of topics, including Ranking, Upsampling and Rendering.
Cha Zhang combines subjects such as Feature, Emotion classification, Face detection and Feature extraction with his study of Speech recognition.
His Face detection study combines topics from a wide range of disciplines, such as Hinge loss and Test set.
His Pattern recognition study integrates concerns from other disciplines, such as Graph signal processing, Probabilistic logic, Probabilistic framework and Face.
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