What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image quality, Visualization and Distortion.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition.
His work deals with themes such as Artificial neural network and Spoofing attack, which intersect with Pattern recognition.
In Computer vision, Shiqi Wang works on issues like Ringing, which are connected to Visual artifact.
The various areas that Shiqi Wang examines in his Image quality study include Image resolution, Data mining, Histogram, Entropy and Image gradient.
His Visualization research incorporates elements of Brightness, Tone mapping and Digital image processing.
His most cited work include:
- Domain Generalization with Adversarial Feature Learning (244 citations)
- SSIM-Motivated Rate-Distortion Optimization for Video Coding (157 citations)
- Saliency-Guided Quality Assessment of Screen Content Images (155 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Coding.
Shiqi Wang frequently studies issues relating to Machine learning and Artificial intelligence.
His work on Data compression, Motion compensation and Human visual system model as part of general Computer vision research is often related to Distortion, thus linking different fields of science.
His studies in Pattern recognition integrate themes in fields like Image, Spoofing attack and Feature.
His work on Coding tree unit and Quadtree as part of general Algorithm study is frequently linked to Rate control and Random access, bridging the gap between disciplines.
His research in Coding intersects with topics in Low complexity, Quantization, Computational complexity theory, Encoder and Reference frame.
He most often published in these fields:
- Artificial intelligence (70.18%)
- Computer vision (31.58%)
- Pattern recognition (25.61%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.18%)
- Pattern recognition (25.61%)
- Coding (22.11%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Coding, Algorithm and Machine learning.
His research on Artificial intelligence often connects related topics like Computer vision.
His Pattern recognition research integrates issues from Regularization, Representation, Face and Spoofing attack.
Shiqi Wang interconnects Data mining, Codec, Encoder, Video quality and Robustness in the investigation of issues within Coding.
His work in the fields of Decoding methods overlaps with other areas such as Random access, Rate control and Block.
His work on Convolutional neural network and Artificial neural network as part of general Machine learning research is frequently linked to Set, bridging the gap between disciplines.
Between 2019 and 2021, his most popular works were:
- Image and Video Compression With Neural Networks: A Review (57 citations)
- Single Image Deraining: From Model-Based to Data-Driven and Beyond. (32 citations)
- Image Quality Assessment: Unifying Structure and Texture Similarity. (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Shiqi Wang focuses on Artificial intelligence, Pattern recognition, Machine learning, Coding and Algorithm.
His study in Convolutional neural network, Deep learning, Feature, Feature extraction and Visualization falls under the purview of Artificial intelligence.
To a larger extent, Shiqi Wang studies Computer vision with the aim of understanding Feature extraction.
Image processing is closely connected to Salience in his research, which is encompassed under the umbrella topic of Computer vision.
His Pattern recognition research includes elements of Pixel, Regularization, Representation and Image restoration.
His study in Coding is interdisciplinary in nature, drawing from both Encoder and Decoding methods.
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