What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Robustness and Convolutional neural network.
His study in Visualization, Deep learning, Feature extraction, Video tracking and Cognitive neuroscience of visual object recognition is done as part of Artificial intelligence.
His work on RGB color model, Image, Camera resectioning and Object is typically connected to Line as part of general Computer vision study, connecting several disciplines of science.
His RGB color model study incorporates themes from Image sensor and Gesture recognition.
His Pattern recognition research is multidisciplinary, relying on both Semantics and Hash function.
The study incorporates disciplines such as Change detection, Filter, Pruning, Algorithm and Convolution in addition to Convolutional neural network.
His most cited work include:
- Enhanced Computer Vision With Microsoft Kinect Sensor: A Review (1123 citations)
- Gabor Convolutional Networks (146 citations)
- Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review (139 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, Convolutional neural network and Image.
His study connects Machine learning and Artificial intelligence.
His Pattern recognition research incorporates elements of Convolution and Representation.
His Computer vision study frequently links to related topics such as Visualization.
In his work, Pruning is strongly intertwined with Algorithm, which is a subfield of Convolutional neural network.
His research investigates the connection between Image and topics such as Hash function that intersect with problems in Binary code.
He most often published in these fields:
- Artificial intelligence (79.23%)
- Pattern recognition (36.15%)
- Computer vision (31.54%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.23%)
- Pattern recognition (36.15%)
- Convolutional neural network (16.92%)
In recent papers he was focusing on the following fields of study:
Jungong Han mostly deals with Artificial intelligence, Pattern recognition, Convolutional neural network, Benchmark and Computer vision.
Feature, Segmentation, Image, RGB color model and Feature extraction are subfields of Artificial intelligence in which his conducts study.
Jungong Han interconnects Image resolution, Field and Deep learning in the investigation of issues within Image.
His work on Discriminative model as part of general Pattern recognition research is frequently linked to Property, bridging the gap between disciplines.
His biological study spans a wide range of topics, including Overhead, Pruning, Network model, Data compression ratio and Algorithm.
Jungong Han has included themes like DUAL and Pattern recognition in his Computer vision study.
Between 2019 and 2021, his most popular works were:
- Attribute-Guided Network for Cross-Modal Zero-Shot Hashing (31 citations)
- Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning (29 citations)
- Episode-Based Prototype Generating Network for Zero-Shot Learning (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Convolutional neural network, Image, Pattern recognition and Feature extraction are his primary areas of study.
His research in Artificial intelligence intersects with topics in Machine learning and Computer vision.
His Convolutional neural network study also includes
- Feature learning which intersects with area such as RGB color model, Salient object detection and Process,
- Artificial neural network which is related to area like Cognitive neuroscience of visual object recognition, Computational intelligence and Convolution.
His Image research incorporates themes from Network model, Deep learning, Reduction and Pruning.
His Pattern recognition research includes themes of Exploit, Pascal and Benchmark.
His work deals with themes such as Feature based, Leverage, Natural language processing, Text retrieval and Iterative method, which intersect with Feature extraction.
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