What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Ling Shao mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Discriminative model.
His research links Machine learning with Artificial intelligence.
His work deals with themes such as Contextual image classification and Kernel, which intersect with Pattern recognition.
His Computer vision research integrates issues from Field and Salient.
His work carried out in the field of Feature extraction brings together such families of science as Feature, Artificial neural network, Speech recognition, Hidden Markov model and Dimensionality reduction.
Ling Shao has researched Discriminative model in several fields, including Subspace topology, Embedding, Probabilistic logic and Robustness.
His most cited work include:
- Enhanced Computer Vision With Microsoft Kinect Sensor: A Review (1123 citations)
- A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior (729 citations)
- A survey on fall detection: Principles and approaches (528 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Discriminative model.
His work in Segmentation, Feature extraction, Deep learning, Feature and Image are all subfields of Artificial intelligence research.
His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Hash function and Robustness.
The Hash function study combines topics in areas such as Binary code and Image retrieval.
His biological study spans a wide range of topics, including Salient and Detector.
His study in Machine learning is interdisciplinary in nature, drawing from both Visualization, Representation, Inference and Training set.
He most often published in these fields:
- Artificial intelligence (76.92%)
- Pattern recognition (35.74%)
- Computer vision (26.04%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (76.92%)
- Pattern recognition (35.74%)
- Machine learning (20.36%)
In recent papers he was focusing on the following fields of study:
Ling Shao spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Discriminative model.
His study looks at the relationship between Artificial intelligence and topics such as Computer vision, which overlap with Artificial neural network.
His study looks at the intersection of Pattern recognition and topics like Embedding with Class.
As a part of the same scientific study, he usually deals with the Machine learning, concentrating on Code and frequently concerns with Algorithm.
The various areas that Ling Shao examines in his Object study include Field and Salient.
His work deals with themes such as Weighting, Pedestrian detection and Feature learning, which intersect with Feature.
Between 2019 and 2021, his most popular works were:
- Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (139 citations)
- Deep Learning for Person Re-identification: A Survey and Outlook (85 citations)
- HRank: Filter Pruning Using High-Rank Feature Map (72 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Ling Shao focuses on Artificial intelligence, Pattern recognition, Segmentation, Discriminative model and Machine learning.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His Pattern recognition research is multidisciplinary, incorporating elements of Embedding, Face, Contextual image classification, Channel and Benchmark.
His Segmentation study which covers Object detection that intersects with Pyramid, Speedup and Feature extraction.
His Discriminative model study combines topics from a wide range of disciplines, such as Semantics, Linear subspace and Visualization.
His research integrates issues of Image resolution and Inference in his study of Machine learning.
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