What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Facial expression and Facial recognition system.
As part of his studies on Artificial intelligence, he frequently links adjacent subjects like Identification.
His Pattern recognition research is multidisciplinary, relying on both Text mining, String and Contextual image classification.
His Facial expression study integrates concerns from other disciplines, such as Orientation, Relation, Feature extraction and Face.
His work focuses on many connections between Object detection and other disciplines, such as Background subtraction, that overlap with his field of interest in Robustness.
His work is dedicated to discovering how Facial Action Coding System, Speech recognition are connected with Surprise and Feature detection and other disciplines.
His most cited work include:
- Comprehensive database for facial expression analysis (2171 citations)
- Recognizing action units for facial expression analysis (1339 citations)
- Recognizing actions using depth motion maps-based histograms of oriented gradients (445 citations)
What are the main themes of his work throughout his whole career to date?
Yingli Tian spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Object detection.
His Artificial intelligence study frequently draws connections to other fields, such as Machine learning.
All of his Computer vision and Object, Background subtraction, Motion, Tracking and Video tracking investigations are sub-components of the entire Computer vision study.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Histogram, Deep learning and Pyramid.
His Feature extraction study which covers Facial recognition system that intersects with Facial Action Coding System.
Yingli Tian interconnects Speech recognition, Face and Gesture in the investigation of issues within Facial expression.
He most often published in these fields:
- Artificial intelligence (77.69%)
- Computer vision (53.46%)
- Pattern recognition (29.23%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (77.69%)
- Pattern recognition (29.23%)
- Deep learning (5.00%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Point cloud.
The study of Artificial intelligence is intertwined with the study of Natural language processing in a number of ways.
His Pattern recognition research incorporates themes from Self supervised learning, Feature and Contrast.
His Deep learning study combines topics in areas such as Scatterometer, Sea ice and Atmospheric model.
His study ties his expertise on Ground-penetrating radar together with the subject of Computer vision.
His work investigates the relationship between Point cloud and topics such as Rendering that intersect with problems in Pyramid, Scale, Solid modeling and Depth map.
Between 2018 and 2021, his most popular works were:
- Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey (226 citations)
- Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey (67 citations)
- Vision-Based Mobile Indoor Assistive Navigation Aid for Blind People (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
His primary areas of investigation include Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Convolutional neural network.
His Artificial intelligence study frequently involves adjacent topics like Computer vision.
His work carried out in the field of Computer vision brings together such families of science as Word and Facial expression.
His Feature extraction, Multiclass classification and Euclidean distance study in the realm of Pattern recognition interacts with subjects such as Ambiguity.
He combines subjects such as Pascal, Segmentation, Image segmentation and Gesture with his study of Convolutional neural network.
His work on Pyramid as part of general Feature study is frequently linked to Pulmonary nodule and Nodule detection, therefore connecting diverse disciplines of science.
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