What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Artificial neural network.
His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Representation.
His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification and Metric.
The various areas that he examines in his Computer vision study include Character, Algorithm design, Word and Robustness.
As a part of the same scientific family, Xiang Bai mostly works in the field of Feature extraction, focusing on Margin and, on occasion, Slope field, Time complexity and Pairwise comparison.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Text mining, Speech recognition and Spotting.
His most cited work include:
- An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition (966 citations)
- Detecting texts of arbitrary orientations in natural images (537 citations)
- AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (519 citations)
What are the main themes of his work throughout his whole career to date?
Xiang Bai mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Object detection and Segmentation.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Pascal.
His research investigates the connection between Pattern recognition and topics such as Feature that intersect with issues in Feature vector.
Computer vision is represented through his Pixel, Object and Topological skeleton research.
His research in Topological skeleton intersects with topics in Algorithm and Skeleton.
His work carried out in the field of Object detection brings together such families of science as Cognitive neuroscience of visual object recognition, Minimum bounding box and Robustness.
He most often published in these fields:
- Artificial intelligence (88.69%)
- Pattern recognition (56.57%)
- Computer vision (28.13%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (88.69%)
- Pattern recognition (56.57%)
- Segmentation (14.98%)
In recent papers he was focusing on the following fields of study:
Xiang Bai focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning.
Xiang Bai interconnects Generator and Machine learning in the investigation of issues within Artificial intelligence.
His Feature extraction study in the realm of Pattern recognition interacts with subjects such as Set.
Xiang Bai combines subjects such as Discriminative model, Sensor array and Data pre-processing with his study of Feature extraction.
The concepts of his Segmentation study are interwoven with issues in Representation, Pascal and Spotting.
His study in the fields of Text detection, Object and Crowd counting under the domain of Computer vision overlaps with other disciplines such as Scale and Conjunction.
Between 2019 and 2021, his most popular works were:
- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection (116 citations)
- Deep-Person: Learning discriminative deep features for person Re-Identification (73 citations)
- Predicting COVID-19 malignant progression with AI techniques (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Xiang Bai mainly focuses on Artificial intelligence, Pattern recognition, Object detection, Segmentation and Semantics.
Xiang Bai has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
His Computer vision study incorporates themes from Vertex and Training set.
Xiang Bai is interested in Convolutional neural network, which is a branch of Pattern recognition.
As a member of one scientific family, Xiang Bai mostly works in the field of Object detection, focusing on Point cloud and, on occasion, Frame rate, Robustness and Data mining.
His research integrates issues of Representation, Spotting and Benchmark in his study of Segmentation.
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