What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Jianke Zhu mainly investigates Artificial intelligence, Machine learning, Computer vision, Image retrieval and Pattern recognition.
His Artificial intelligence and Video tracking, Benchmark, Eye tracking, Semi-supervised learning and Cluster analysis investigations all form part of his Artificial intelligence research activities.
His Eye tracking study incorporates themes from Adaptive kernel, Kanade–Lucas–Tomasi feature tracker, Kernel and Robustness.
His research in Computer vision is mostly focused on Minimum bounding box.
Jianke Zhu has included themes like Deep learning, Convolutional neural network and Information retrieval in his Image retrieval study.
His study on Scale-invariant feature transform is often connected to Hypergraph as part of broader study in Pattern recognition.
His most cited work include:
- A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (790 citations)
- Deep Learning for Content-Based Image Retrieval: A Comprehensive Study (565 citations)
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image retrieval.
Deep learning, Feature, Feature extraction, Tracking and Benchmark are among the areas of Artificial intelligence where Jianke Zhu concentrates his study.
His research in Pattern recognition intersects with topics in Facial recognition system, Face and Image.
His work on Image segmentation, Object, Image processing and Minimum bounding box is typically connected to Scale as part of general Computer vision study, connecting several disciplines of science.
His study looks at the intersection of Machine learning and topics like Representation with Click-through rate.
His Image retrieval research is multidisciplinary, relying on both Annotation, Sparse approximation and Information retrieval.
He most often published in these fields:
- Artificial intelligence (82.73%)
- Pattern recognition (40.00%)
- Computer vision (32.73%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (82.73%)
- Deep learning (12.73%)
- Machine learning (24.55%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Deep learning, Machine learning, Computer vision and Tracking.
His work on Pattern recognition expands to the thematically related Artificial intelligence.
The various areas that Jianke Zhu examines in his Pattern recognition study include Range, Contrast and Image.
His Deep learning research is multidisciplinary, incorporating perspectives in Artificial neural network, Ground truth and Feature vector.
His work in the fields of Computer vision, such as Point cloud and Correlation filter, intersects with other areas such as Scale, Set and Selection.
In his study, Video tracking is inextricably linked to Minimum bounding box, which falls within the broad field of Feature extraction.
Between 2018 and 2021, his most popular works were:
- Feature Agglomeration Networks for Single Stage Face Detection (30 citations)
- Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking (27 citations)
- Robust estimation of similarity transformation for visual object tracking (24 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, Video tracking, Tracking, Computer vision and Athletic training.
Artificial intelligence is often connected to Click-through rate in his work.
Jianke Zhu regularly ties together related areas like Rotation in his Video tracking studies.
The study incorporates disciplines such as Algorithm, Machine learning and Solver in addition to Tracking.
His work carried out in the field of Computer vision brings together such families of science as Regularization and Boosting.
Automatic summarization, Relation, Eye tracking, Pose and Human–computer interaction are fields of study that overlap with his Athletic training research.
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