What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Siwei Ma focuses on Artificial intelligence, Computer vision, Algorithm, Coding tree unit and Discrete cosine transform.
His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition.
His Computer vision study combines topics in areas such as Distortion, Video quality and Augmented Lagrangian method.
His Algorithm research includes themes of View synthesis and Rate–distortion optimization.
His work carried out in the field of Coding tree unit brings together such families of science as Multiview Video Coding, Speedup and Context-adaptive binary arithmetic coding.
His Discrete cosine transform study deals with Speech recognition intersecting with Normalization, Block code and Iterative method.
His most cited work include:
- Rate-distortion analysis for H.264/AVC video coding and its application to rate control (289 citations)
- Fast mode decision algorithm for intra prediction in HEVC (260 citations)
- SSIM-Motivated Rate-Distortion Optimization for Video Coding (157 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Coding tree unit.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pixel, Motion compensation, Data compression, Iterative reconstruction and Image quality.
In most of his Computer vision studies, his work intersects topics such as Distortion.
His research in Algorithm intersects with topics in Real-time computing, Rate–distortion optimization and Discrete cosine transform.
His research in Pattern recognition focuses on subjects like Image restoration, which are connected to Regularization.
His Coding tree unit course of study focuses on Context-adaptive binary arithmetic coding and Sub-band coding and Macroblock.
He most often published in these fields:
- Artificial intelligence (55.93%)
- Computer vision (41.79%)
- Algorithm (35.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (55.93%)
- Algorithm (35.55%)
- Computer vision (41.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Algorithm, Computer vision, Deep learning and Decoding methods.
Siwei Ma has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
Siwei Ma has researched Pattern recognition in several fields, including Distortion and Feature.
As part of his studies on Algorithm, Siwei Ma frequently links adjacent subjects like Random access.
The concepts of his Computer vision study are interwoven with issues in Visualization and Codec.
His research integrates issues of Fingerprint, Speaker recognition, Speech recognition and Biometrics in his study of Deep learning.
Between 2017 and 2021, his most popular works were:
- Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (107 citations)
- Content-Aware Convolutional Neural Network for In-Loop Filtering in High Efficiency Video Coding (58 citations)
- Image and Video Compression With Neural Networks: A Review (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Siwei Ma spends much of his time researching Artificial intelligence, Algorithm, Deep learning, Computer vision and Convolutional neural network.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
His research in Algorithm tackles topics such as Random access which are related to areas like Interleaving and Decoding methods.
His studies deal with areas such as Image processing, Image, Reference frame, Absolute value and Cross entropy as well as Deep learning.
Siwei Ma interconnects Visualization and Solid modeling in the investigation of issues within Computer vision.
His Motion compensation research integrates issues from Image resolution and Motion field.
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