What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, RGB color model and Cognitive neuroscience of visual object recognition.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Multivariate statistics.
In Pattern recognition, Liefeng Bo works on issues like Contextual image classification, which are connected to Iterative reconstruction.
Liefeng Bo has included themes like Kernel and Feature vector in his Computer vision study.
His RGB color model research focuses on subjects like Scene labeling, which are linked to Point cloud.
His Cognitive neuroscience of visual object recognition research is multidisciplinary, incorporating elements of Visualization and Feature.
His most cited work include:
- A large-scale hierarchical multi-view RGB-D object dataset (983 citations)
- RGB-(D) scene labeling: Features and algorithms (375 citations)
- Multiobjective immune algorithm with nondominated neighbor-based selection (343 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Support vector machine and Machine learning.
His Cognitive neuroscience of visual object recognition, Feature extraction, Image, RGB color model and Object investigations are all subjects of Artificial intelligence research.
His work deals with themes such as Contextual image classification, Kernel and Kernel, which intersect with Pattern recognition.
In his study, which falls under the umbrella issue of Computer vision, Coherence, Point cloud and Segmentation is strongly linked to Code.
His studies deal with areas such as Algorithm, Mathematical optimization and Selection as well as Support vector machine.
Many of his research projects under Machine learning are closely connected to Scalability with Scalability, tying the diverse disciplines of science together.
He most often published in these fields:
- Artificial intelligence (77.89%)
- Pattern recognition (40.00%)
- Computer vision (34.74%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (77.89%)
- Computer vision (34.74%)
- Process (5.26%)
In recent papers he was focusing on the following fields of study:
Liefeng Bo focuses on Artificial intelligence, Computer vision, Process, Image and Data mining.
His Artificial intelligence study is mostly concerned with Object detection, Convolutional neural network, Feature extraction, Language model and Artificial neural network.
His work on Feature as part of general Computer vision research is often related to Drone, thus linking different fields of science.
His Image study combines topics from a wide range of disciplines, such as Dimension and Depth mapping.
His studies in Data mining integrate themes in fields like Confidence value and Sensor fusion.
Discriminative model is a subfield of Pattern recognition that he tackles.
Between 2018 and 2021, his most popular works were:
- ScratchDet: Training Single-Shot Object Detectors From Scratch (54 citations)
- Spatiotemporal CNN for Video Object Segmentation (34 citations)
- VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Computer vision, Drone, Object detection and Process.
His study deals with a combination of Artificial intelligence and Scratch.
His Drone investigation overlaps with other areas such as Video tracking, Field, Tracking, Visualization and Crowd counting.
His Object detection research includes themes of Machine learning and Image.
His Process studies intersect with other subjects such as Feature, Segmentation, Code, Object and Coherence.
His research integrates issues of Crowds, Perspective and Discriminative model in his study of Feature.
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