What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Bohyung Han mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Segmentation.
His studies in Benchmark, Video tracking, Artificial neural network, Feature extraction and Object detection are all subfields of Artificial intelligence research.
His work in Discriminative model and Image segmentation is related to Pattern recognition.
His Convolutional neural network research integrates issues from Construct, Eye tracking and Algorithm.
His studies deal with areas such as Kernel embedding of distributions, Kernel method and Variable kernel density estimation as well as Computer vision.
Bohyung Han usually deals with Segmentation and limits it to topics linked to Pascal and Deconvolution, Segmentation-based object categorization and Scale-space segmentation.
His most cited work include:
- Learning Deconvolution Network for Semantic Segmentation (1938 citations)
- Learning Multi-domain Convolutional Neural Networks for Visual Tracking (1417 citations)
- Learning Deconvolution Network for Semantic Segmentation (619 citations)
What are the main themes of his work throughout his whole career to date?
Bohyung Han focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Convolutional neural network.
His study in Artificial intelligence concentrates on Segmentation, Video tracking, Tracking, Benchmark and Image.
His work carried out in the field of Segmentation brings together such families of science as Deconvolution and Pascal.
His research investigates the connection between Pattern recognition and topics such as Eye tracking that intersect with issues in Representation.
His Machine learning study incorporates themes from Question answering and Contextual image classification.
The Convolutional neural network study combines topics in areas such as Artificial neural network, Construct and Feature.
He most often published in these fields:
- Artificial intelligence (90.38%)
- Pattern recognition (40.38%)
- Computer vision (34.62%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.38%)
- Benchmark (13.46%)
- Computer vision (34.62%)
In recent papers he was focusing on the following fields of study:
Bohyung Han mainly focuses on Artificial intelligence, Benchmark, Computer vision, Segmentation and Communication channel.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing, Machine learning and Pattern recognition.
His Machine learning study combines topics from a wide range of disciplines, such as Class and Construct.
As part of his studies on Pattern recognition, Bohyung Han often connects relevant areas like Object detection.
His work in the fields of Computer vision, such as Object, Tracking and Quantization, intersects with other areas such as Jpeg image compression.
His Segmentation research is multidisciplinary, incorporating elements of Minimum bounding box and Exemplar theory.
Between 2019 and 2021, his most popular works were:
- Channel Attention Is All You Need for Video Frame Interpolation (26 citations)
- Local-Global Video-Text Interactions for Temporal Grounding (11 citations)
- Towards Oracle Knowledge Distillation with Neural Architecture Search (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Communication channel, Differentiable function, Algorithm and Normalization are his primary areas of study.
The various areas that he examines in his Artificial intelligence study include Machine learning and Natural language processing.
His Natural language processing research is multidisciplinary, relying on both Image and Closed captioning.
His work deals with themes such as Object, Tracking and Video tracking, which intersect with Pruning.
His Computer vision study integrates concerns from other disciplines, such as Artificial neural network and Benchmark.
Bohyung Han combines subjects such as Motion interpolation and Motion with his study of Frame.
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