What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Feature extraction, Hyperspectral imaging and Computer vision.
Artificial intelligence is closely attributed to Machine learning in his research.
In Pattern recognition, Qi Wang works on issues like Cluster analysis, which are connected to Linear programming.
He has researched Feature extraction in several fields, including Semi-supervised learning, Perspective and Markov chain.
His work carried out in the field of Hyperspectral imaging brings together such families of science as Image, Data mining, Support vector machine and Dimensionality reduction.
His study in the field of Optical flow also crosses realms of Intelligent transportation system.
His most cited work include:
- Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (297 citations)
- Scene Classification With Recurrent Attention of VHR Remote Sensing Images (163 citations)
- Learning From Synthetic Data for Crowd Counting in the Wild (148 citations)
What are the main themes of his work throughout his whole career to date?
Qi Wang focuses on Artificial intelligence, Pattern recognition, Computer vision, Hyperspectral imaging and Image.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Similarity, Feature and Cluster analysis.
His Hyperspectral imaging research is multidisciplinary, incorporating elements of Spatial analysis, Multispectral image, Anomaly detection, Support vector machine and Iterative reconstruction.
His studies deal with areas such as Remote sensing, Representation and Constraint as well as Image.
He combines subjects such as Feature selection and Data set with his study of Feature extraction.
He most often published in these fields:
- Artificial intelligence (66.32%)
- Pattern recognition (34.74%)
- Computer vision (21.75%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (66.32%)
- Pattern recognition (34.74%)
- Image (15.09%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Image, Computer vision and Hyperspectral imaging.
The Artificial intelligence study combines topics in areas such as Focus and Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as RGB color model, Similarity, Feature and Cluster analysis.
His Image research includes elements of Remote sensing and Convolutional neural network.
When carried out as part of a general Computer vision research project, his work on Optical flow and Object is frequently linked to work in License, therefore connecting diverse disciplines of study.
Qi Wang focuses mostly in the field of Hyperspectral imaging, narrowing it down to topics relating to Multispectral image and, in certain cases, Image fusion and Image resolution.
Between 2019 and 2021, his most popular works were:
- Detecting Coherent Groups in Crowd Scenes by Multiview Clustering (143 citations)
- PCC Net: Perspective Crowd Counting via Spatial Convolutional Network (47 citations)
- NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization. (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Hyperspectral imaging.
As part of his studies on Artificial intelligence, Qi Wang often connects relevant subjects like Focus.
His work in the fields of Feature learning overlaps with other areas such as Electronic mail.
His Algorithm research focuses on Robustness and how it relates to Crowds, Consistency, Anomaly detection and Motion detection.
His Machine learning research includes themes of Object, Energy and Generalization.
He has included themes like Data processing, Multispectral image, Redundancy and Spectral bands in his Hyperspectral imaging study.
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