What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Zicheng Liu mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Computer graphics and Feature extraction.
Zicheng Liu combines subjects such as Machine learning and Graph with his study of Artificial intelligence.
His Computer vision study integrates concerns from other disciplines, such as Computer facial animation, Animation, Facial expression and Hidden Markov model.
His work deals with themes such as Depth map and Histogram, which intersect with Pattern recognition.
His study in Computer graphics is interdisciplinary in nature, drawing from both Image correction, Image stitching, Face model and Table.
His work is dedicated to discovering how Feature extraction, Robustness are connected with Invariant and other disciplines.
His most cited work include:
- Mining actionlet ensemble for action recognition with depth cameras (1170 citations)
- Action recognition based on a bag of 3D points (1050 citations)
- HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences (726 citations)
What are the main themes of his work throughout his whole career to date?
Zicheng Liu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Speech recognition and Computer graphics.
While the research belongs to areas of Artificial intelligence, Zicheng Liu spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Training set.
His Computer vision research includes elements of Facial expression and Computer graphics.
Zicheng Liu has researched Facial expression in several fields, including Computer facial animation, Animation and Geometry.
His Pattern recognition research integrates issues from Object detection and Robustness.
His Speech recognition research is multidisciplinary, incorporating elements of Speech enhancement and Microphone.
He most often published in these fields:
- Artificial intelligence (65.00%)
- Computer vision (43.93%)
- Pattern recognition (20.71%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (65.00%)
- Computer vision (43.93%)
- Pattern recognition (20.71%)
In recent papers he was focusing on the following fields of study:
Zicheng Liu spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Machine learning.
In his work, Pattern recognition is strongly intertwined with Field, which is a subfield of Computer vision.
His Pattern recognition research incorporates themes from Pyramid, Feature, Feature, Object detection and Minimum bounding box.
In his study, Feature extraction is inextricably linked to Robustness, which falls within the broad field of Pyramid.
Zicheng Liu interconnects Polygon mesh, Parametric statistics, Artificial neural network, Activation function and Convolution in the investigation of issues within Algorithm.
His biological study deals with issues like Forgetting, which deal with fields such as Missing data, Incremental learning, Adversarial system, Regularization and Generative grammar.
Between 2015 and 2021, his most popular works were:
- Survey on 3D Hand Gesture Recognition (171 citations)
- Large Scale Incremental Learning (165 citations)
- HP-GAN: Probabilistic 3D Human Motion Prediction via GAN (67 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Zicheng Liu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Representation.
His study in Convolutional neural network, Classifier, Pattern recognition, Feature extraction and Gesture recognition falls within the category of Artificial intelligence.
As part of the same scientific family, Zicheng Liu usually focuses on Feature extraction, concentrating on Segmentation and intersecting with Deep learning.
When carried out as part of a general Computer vision research project, his work on Motion is frequently linked to work in Vertex, therefore connecting diverse disciplines of study.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Supervised learning, Feature and Feature.
His Machine learning course of study focuses on Forgetting and Layer, Missing data, Incremental learning, Linear model and Face.
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