What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Image and Object.
His research combines Pattern recognition and Artificial intelligence.
His studies examine the connections between Computer vision and genetics, as well as such issues in Perspective, with regards to Image processing.
His Machine learning research is multidisciplinary, incorporating perspectives in Method, Feature extraction and Pascal.
His Image research incorporates themes from Visualization, Segmentation and Knowledge extraction.
His Object research is multidisciplinary, relying on both Computer graphics and Statistical model.
His most cited work include:
- Indoor segmentation and support inference from RGBD images (2739 citations)
- Describing objects by their attributes (1592 citations)
- Putting Objects in Perspective (826 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 Artificial intelligence study often links to related topics such as Machine learning.
His Computer vision research incorporates elements of Perspective and Inference.
His work on Support vector machine as part of general Pattern recognition research is often related to Set, thus linking different fields of science.
His Image study combines topics in areas such as Enhanced Data Rates for GSM Evolution, Geometry, Surface and Computer graphics.
His Object research includes elements of Visual reasoning and Representation.
He most often published in these fields:
- Artificial intelligence (84.43%)
- Computer vision (46.72%)
- Pattern recognition (31.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (84.43%)
- Image (22.13%)
- Pattern recognition (31.97%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Image, Pattern recognition, Computer vision and Type.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Natural language processing.
He has researched Image in several fields, including State and Computer graphics.
His Pattern recognition research includes themes of Real image and Task.
Derek Hoiem works mostly in the field of Object, limiting it down to concerns involving Surface and, occasionally, Robustness.
The concepts of his Classifier study are interwoven with issues in Contextual image classification and Inference.
Between 2018 and 2021, his most popular works were:
- Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion (54 citations)
- No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques (43 citations)
- Complete 3D Scene Parsing from an RGBD Image (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Derek Hoiem mainly focuses on Artificial intelligence, Pattern recognition, Task analysis, Knowledge transfer and Artificial neural network.
His work on Object, Word and Mutual information as part of general Artificial intelligence study is frequently linked to Code and Construct, bridging the gap between disciplines.
The study of Object is intertwined with the study of Pattern recognition in a number of ways.
Bayesian inference, Machine learning, Inference, Classifier and Training set are fields of study that overlap with his Task analysis research.
His Knowledge transfer research includes a combination of various areas of study, such as Normalization, Real image, Normalization and Divergence.
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.
