What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Zhen Lei spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Face and Facial recognition system.
As a part of the same scientific study, Zhen Lei usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Field.
Zhen Lei combines subjects such as Outlier, Re identification and Deviance with his study of Pattern recognition.
When carried out as part of a general Computer vision research project, his work on Histogram and 3D pose estimation is frequently linked to work in Detector and Hypergraph, therefore connecting diverse disciplines of study.
His Face study combines topics in areas such as Feature, Feature, Pixel, Representation and Image.
His Three-dimensional face recognition study in the realm of Facial recognition system interacts with subjects such as Liveness.
His most cited work include:
- Learning Face Representation from Scratch (1003 citations)
- Face Alignment Across Large Poses: A 3D Solution (640 citations)
- Single-Shot Refinement Neural Network for Object Detection (541 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Facial recognition system, Computer vision and Face are his primary areas of study.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Pattern recognition research incorporates elements of Pixel, Subspace topology, Deep learning and Feature.
He has researched Facial recognition system in several fields, including Feature, Local binary patterns, Spoofing attack and Biometrics.
Many of his research projects under Computer vision are closely connected to Detector with Detector, tying the diverse disciplines of science together.
The various areas that Zhen Lei examines in his Face study include Image, Representation, Database and Benchmark.
He most often published in these fields:
- Artificial intelligence (90.77%)
- Pattern recognition (55.35%)
- Facial recognition system (43.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.77%)
- Facial recognition system (43.17%)
- Face (34.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Facial recognition system, Face, Pattern recognition and Machine learning.
In his research on the topic of Artificial intelligence, Robustness is strongly related with Computer vision.
The concepts of his Facial recognition system study are interwoven with issues in Margin and Sample.
His research in Face intersects with topics in Image, Spoofing attack and Code.
His work deals with themes such as RGB color model, Object and Modality, which intersect with Pattern recognition.
As a part of the same scientific family, Zhen Lei mostly works in the field of Machine learning, focusing on Representation and, on occasion, Encoder, Mutual information and Face hallucination.
Between 2019 and 2021, his most popular works were:
- Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection (128 citations)
- UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking (118 citations)
- Vehicle Re-Identification Using Quadruple Directional Deep Learning Features (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Facial recognition system, Face, Pattern recognition and Benchmark.
The various areas that Zhen Lei examines in his Artificial intelligence study include Margin and Computer vision.
Zhen Lei interconnects Feature learning and Robustness in the investigation of issues within Computer vision.
His Facial recognition system study integrates concerns from other disciplines, such as Machine learning and Spoofing attack.
His Face study combines topics from a wide range of disciplines, such as Stability and Code.
His study in Feature extraction and Discriminative model is done as part of Pattern recognition.
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