What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Convolutional neural network.
His studies link Computer vision with Artificial intelligence.
In general Pattern recognition, his work in Normalization is often linked to Action recognition linking many areas of study.
His Machine learning research includes themes of Context, Structure, Key and Process.
His Discriminative model research incorporates themes from Artificial neural network and Data mining.
His Convolutional neural network study combines topics in areas such as Facial recognition system, Confusion matrix, Multi resolution and Knowledge engineering.
His most cited work include:
- Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks (2105 citations)
- A Discriminative Feature Learning Approach for Deep Face Recognition (1894 citations)
- Temporal Segment Networks: Towards Good Practices for Deep Action Recognition (1578 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Convolutional neural network.
His work in Image, Deep learning, Discriminative model, Feature extraction and Feature are all subfields of Artificial intelligence research.
His research ties Feature and Discriminative model together.
Yu Qiao has included themes like Facial recognition system, Face, Speech recognition and Representation in his Pattern recognition study.
His study on Computer vision is mostly dedicated to connecting different topics, such as Feature vector.
Margin and Transfer of learning are subfields of Machine learning in which his conducts study.
He most often published in these fields:
- Artificial intelligence (73.62%)
- Pattern recognition (40.75%)
- Computer vision (24.61%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (73.62%)
- Pattern recognition (40.75%)
- Computer vision (24.61%)
In recent papers he was focusing on the following fields of study:
Yu Qiao mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Image and Machine learning.
His biological study deals with issues like Code, which deal with fields such as Object.
He has researched Pattern recognition in several fields, including Representation, Feature and Facial expression.
His work deals with themes such as Graph and Graph, which intersect with Computer vision.
In general Image study, his work on Superresolution often relates to the realm of Process, thereby connecting several areas of interest.
The concepts of his Machine learning study are interwoven with issues in Object detection and Detector.
Between 2019 and 2021, his most popular works were:
- Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (65 citations)
- Suppressing Uncertainties for Large-Scale Facial Expression Recognition (28 citations)
- Fabricating better metal-organic frameworks separators for Li–S batteries: Pore sizes effects inspired channel modification strategy (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Yu Qiao spends much of his time researching Artificial intelligence, Pattern recognition, Chemical engineering, Computer vision and Image.
His biological study spans a wide range of topics, including Machine learning and Generalization.
His work on Feature extraction as part of general Pattern recognition study is frequently linked to Domain, bridging the gap between disciplines.
His study in the field of Pyrolysis also crosses realms of Lower temperature.
The study incorporates disciplines such as Focus and Interpolation in addition to Computer vision.
His Image research includes elements of Artificial neural network and Noisy data.
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