What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Jonathan Brandt mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Face detection and Facial recognition system.
His study in Linear search extends to Artificial intelligence with its themes.
His work in the fields of Nearest neighbor search overlaps with other areas such as Ball tree.
He studies Feature, a branch of Computer vision.
His Facial recognition system study integrates concerns from other disciplines, such as Linear programming, Feature extraction and Standard test image.
His work in Discriminative model addresses subjects such as Convolutional neural network, which are connected to disciplines such as Hierarchical network model, Classifier, Phrase and Backpropagation.
His most cited work include:
- A convolutional neural network cascade for face detection (863 citations)
- Interactive facial feature localization (668 citations)
- Robust object detection via soft cascade (337 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Image and Object are his primary areas of study.
His study in Classifier, Convolutional neural network, Feature, Discriminative model and Face is carried out as part of his Artificial intelligence studies.
He works mostly in the field of Discriminative model, limiting it down to topics relating to Probabilistic logic and, in certain cases, Backpropagation, Hierarchical network model and Phrase, as a part of the same area of interest.
Computer vision is closely attributed to Visual search in his study.
In his study, which falls under the umbrella issue of Pattern recognition, Identification is strongly linked to Font.
His research in Image intersects with topics in Function, Pixel and Information retrieval.
He most often published in these fields:
- Artificial intelligence (71.09%)
- Computer vision (46.88%)
- Pattern recognition (46.09%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (71.09%)
- Reinforcement learning (3.91%)
- Action (3.91%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Action, Computer vision and Image.
Jonathan Brandt has included themes like Natural language processing and Pattern recognition in his Artificial intelligence study.
His Pattern recognition research focuses on Hierarchical network model and how it connects with Convolutional neural network.
In general Computer vision, his work in Tracking and Object is often linked to Track and Key linking many areas of study.
His Image study combines topics in areas such as Control, Service and Content sharing.
His Classifier research incorporates elements of Backpropagation and Phrase.
Between 2016 and 2021, his most popular works were:
- Top-Down Neural Attention by Excitation Backprop (241 citations)
- Spatial-Semantic Image Search by Visual Feature Synthesis (20 citations)
- Hotels-50K: A Global Hotel Recognition Dataset (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial neural network, Artificial intelligence, Feature, Information retrieval and Convolutional neural network.
His Artificial neural network study incorporates themes from Visualization, Visual Word and Pattern recognition.
As part of his studies on Artificial intelligence, Jonathan Brandt frequently links adjacent subjects like Computer vision.
His research in the fields of Feature set overlaps with other disciplines such as Term, Spatial analysis, Visual media and User input.
His Information retrieval research is multidisciplinary, relying on both Data modeling, Feature extraction and Image retrieval.
His studies deal with areas such as Classifier, Probabilistic logic, Hierarchical network model, Discriminative model and Phrase as well as Convolutional neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
