What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Frédéric Jurie mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Contextual image classification and Object detection.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His research on Pattern recognition frequently links to adjacent areas such as Parametric statistics.
His Computer vision research includes themes of Representation and Task.
His Contextual image classification research is multidisciplinary, relying on both Support vector machine, Cluster analysis and Standard test image.
The study incorporates disciplines such as Object-class detection, Image segmentation, Edge detection and Pascal in addition to Object detection.
His most cited work include:
- Sampling strategies for bag-of-features image classification (803 citations)
- Creating efficient codebooks for visual recognition (657 citations)
- PCCA: A new approach for distance learning from sparse pairwise constraints (555 citations)
What are the main themes of his work throughout his whole career to date?
Frédéric Jurie mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Contextual image classification.
His work in Discriminative model, Object detection, Image, Embedding and Face is related to Artificial intelligence.
His Object detection research incorporates themes from Image segmentation and Pattern recognition.
His research in Pattern recognition intersects with topics in Histogram and Visual Word, Image retrieval.
Frédéric Jurie works mostly in the field of Machine learning, limiting it down to topics relating to Classifier and, in certain cases, Training set, as a part of the same area of interest.
Frédéric Jurie has included themes like Class, Pascal, Cluster analysis and Standard test image in his Contextual image classification study.
He most often published in these fields:
- Artificial intelligence (89.78%)
- Pattern recognition (47.85%)
- Computer vision (39.25%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (89.78%)
- Machine learning (25.27%)
- Computer vision (39.25%)
In recent papers he was focusing on the following fields of study:
Frédéric Jurie mostly deals with Artificial intelligence, Machine learning, Computer vision, Embedding and Deep learning.
Frédéric Jurie integrates Artificial intelligence and Space in his studies.
His Machine learning study integrates concerns from other disciplines, such as Classifier and Variety.
The concepts of his Classifier study are interwoven with issues in Model selection, Curse of dimensionality, Occam's razor and Test set.
Many of his research projects under Computer vision are closely connected to Scale and Bottleneck with Scale and Bottleneck, tying the diverse disciplines of science together.
In his research, Task analysis and Feature extraction is intimately related to Adversarial system, which falls under the overarching field of Representation.
Between 2017 and 2021, his most popular works were:
- Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks (49 citations)
- MFAS: Multimodal Fusion Architecture Search (47 citations)
- An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Machine learning, Computer vision, Deep learning and Feature learning.
He performs multidisciplinary studies into Artificial intelligence and Multimodal fusion in his work.
The Machine learning study combines topics in areas such as Embedding and Modality.
His Computer vision study combines topics from a wide range of disciplines, such as End-to-end principle and Data set.
His studies deal with areas such as Classifier, Model selection and Curse of dimensionality as well as Deep learning.
His Feature learning research integrates issues from Representation, Inference, Data visualization and Dimension.
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