What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Image processing.
His Artificial intelligence research integrates issues from Sketch and Machine learning.
The various areas that he examines in his Pattern recognition study include Embedding and Histogram, Image.
The Computer vision study combines topics in areas such as Distortion and Robustness.
His biological study spans a wide range of topics, including Pixel and Algorithm.
Xinbo Gao interconnects Image resolution, Regularization and Convolutional neural network in the investigation of issues within Iterative reconstruction.
His most cited work include:
- A survey of graph edit distance (448 citations)
- Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression (344 citations)
- Ordinal Regression with Multiple Output CNN for Age Estimation (294 citations)
What are the main themes of his work throughout his whole career to date?
Xinbo Gao spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Image and Feature extraction.
His Artificial intelligence study frequently draws connections to other fields, such as Machine learning.
His work carried out in the field of Pattern recognition brings together such families of science as Deep learning, Representation and Cluster analysis.
His research integrates issues of Algorithm and Data mining in his study of Cluster analysis.
He combines topics linked to Robustness with his work on Computer vision.
His Image quality study incorporates themes from Distortion, Metric and Human visual system model.
He most often published in these fields:
- Artificial intelligence (81.03%)
- Pattern recognition (49.59%)
- Computer vision (30.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (81.03%)
- Pattern recognition (49.59%)
- Image (18.16%)
In recent papers he was focusing on the following fields of study:
Xinbo Gao mainly focuses on Artificial intelligence, Pattern recognition, Image, Convolutional neural network and Feature.
Much of his study explores Artificial intelligence relationship to Computer vision.
His Pattern recognition research includes themes of Image quality and Cluster analysis.
His Image research focuses on Code and how it relates to Pixel.
His Convolutional neural network research integrates issues from Motion estimation and Function.
His study in Feature is interdisciplinary in nature, drawing from both Channel and Computation.
Between 2019 and 2021, his most popular works were:
- Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution (60 citations)
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results (20 citations)
- Neural Probabilistic Graphical Model for Face Sketch Synthesis (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Image, Computer vision and Artificial neural network.
His studies link Task with Artificial intelligence.
His research in Pattern recognition intersects with topics in Image quality, Pixel, Feature and Metric.
The study incorporates disciplines such as Generator, Focus, Representation and Benchmark in addition to Image.
His Computer vision research is multidisciplinary, incorporating perspectives in Sketch, Weighting and Robustness.
His research on Artificial neural network also deals with topics like
- Adversarial system, which have a strong connection to Iterative method, Entropy and Theoretical computer science,
- Modality, which have a strong connection to Constraint, Missing data and Generalization.
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