What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Convolutional neural network.
Artificial intelligence is represented through his Segmentation, Feature extraction, Artificial neural network, Discriminative model and Pascal research.
His Pattern recognition study combines topics in areas such as Feature, Pixel, Contextual image classification, Facial recognition system and Visualization.
His Computer vision research is multidisciplinary, incorporating perspectives in Support vector machine and Robustness.
His Machine learning study which covers Benchmark that intersects with Recurrent neural network.
Chunhua Shen usually deals with Convolutional neural network and limits it to topics linked to Conditional random field and Monocular.
His most cited work include:
- RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation (1131 citations)
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields (665 citations)
- Supervised Discrete Hashing (630 citations)
What are the main themes of his work throughout his whole career to date?
Chunhua Shen mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Computer vision.
Convolutional neural network, Feature, Image, Discriminative model and Feature extraction are subfields of Artificial intelligence in which his conducts study.
His Pattern recognition research integrates issues from Object detection, Pixel, Representation, Contextual image classification and Benchmark.
His studies deal with areas such as Classifier and Training set as well as Machine learning.
In general Segmentation study, his work on Image segmentation often relates to the realm of Code, thereby connecting several areas of interest.
His Computer vision research is multidisciplinary, relying on both Support vector machine and Robustness.
He most often published in these fields:
- Artificial intelligence (79.26%)
- Pattern recognition (43.03%)
- Machine learning (21.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.26%)
- Pattern recognition (43.03%)
- Segmentation (17.18%)
In recent papers he was focusing on the following fields of study:
Chunhua Shen mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Code and Image.
His work deals with themes such as Machine learning and Computer vision, which intersect with Artificial intelligence.
His studies in Machine learning integrate themes in fields like Anomaly detection and Memorization.
Chunhua Shen studies Convolutional neural network which is a part of Pattern recognition.
His Image segmentation study in the realm of Segmentation interacts with subjects such as Layer.
His Image study combines topics in areas such as Artificial neural network and Metric.
Between 2019 and 2021, his most popular works were:
- COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection (169 citations)
- PolarMask: Single Shot Instance Segmentation With Polar Representation (104 citations)
- Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection (69 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Chunhua Shen spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Object detection and Feature extraction.
He merges Artificial intelligence with Code in his research.
While the research belongs to areas of Pattern recognition, Chunhua Shen spends his time largely on the problem of Margin, intersecting his research to questions surrounding Ordinal regression and Training set.
His work on Image segmentation is typically connected to Layer as part of general Segmentation study, connecting several disciplines of science.
His Object detection research is multidisciplinary, incorporating perspectives in Kernel, Contextual image classification and Matrix multiplication.
Chunhua Shen has researched Feature extraction in several fields, including Computer engineering, Encoding, Minimum bounding box, Task analysis and Benchmark.
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