What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Cognitive neuroscience of visual object recognition, Pattern recognition and Cluster analysis.
His biological study spans a wide range of topics, including Structure, Machine learning and Set.
In his research, Sequence is intimately related to Computer graphics, which falls under the overarching field of Computer vision.
He has researched Cognitive neuroscience of visual object recognition in several fields, including Object model, Texture, Invariant and Image retrieval.
His work deals with themes such as Contextual image classification, Viola–Jones object detection framework and Machine translation, which intersect with Pattern recognition.
The various areas that David Forsyth examines in his Cluster analysis study include Facial recognition system, Entropy and Natural language.
His most cited work include:
- Computer Vision: A Modern Approach (2811 citations)
- Describing objects by their attributes (1592 citations)
- Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary (1512 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Image and Cognitive neuroscience of visual object recognition.
David Forsyth combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence.
His Computer vision study integrates concerns from other disciplines, such as Representation and Shading, Computer graphics.
His Pattern recognition study incorporates themes from Real image and Set.
His Image study is mostly concerned with Texture and Contextual image classification.
His studies in Cognitive neuroscience of visual object recognition integrate themes in fields like Object model and Image retrieval.
He most often published in these fields:
- Artificial intelligence (60.36%)
- Computer vision (34.20%)
- Pattern recognition (15.80%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (60.36%)
- Image (15.03%)
- Computer vision (34.20%)
In recent papers he was focusing on the following fields of study:
David Forsyth mostly deals with Artificial intelligence, Image, Computer vision, Pattern recognition and Machine learning.
His Natural language processing research extends to the thematically linked field of Artificial intelligence.
His Natural language processing study combines topics in areas such as Beam search, Word, Matching and Closed captioning.
His Image research is multidisciplinary, incorporating perspectives in Pixel and Deep learning, Autoencoder.
He interconnects Embedding, Representation, Field and Shading in the investigation of issues within Computer vision.
His work is dedicated to discovering how Embedding, Information retrieval are connected with Set and other disciplines.
Between 2016 and 2021, his most popular works were:
- SafetyNet: Detecting and Rejecting Adversarial Examples Robustly (163 citations)
- NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles (159 citations)
- Learning Diverse Image Colorization (92 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
David Forsyth spends much of his time researching Artificial intelligence, Image, Adversarial system, Closed captioning and Computer vision.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His Image research is multidisciplinary, relying on both Pixel and Autoencoder.
His Adversarial system study also includes fields such as
- Theoretical computer science, which have a strong connection to Visual descriptors and Bounding overwatch,
- Robustness most often made with reference to Depth map,
- Artificial neural network that connect with fields like Object detection.
His research in Closed captioning tackles topics such as Natural language processing which are related to areas like Beam search.
His study in Computer vision is interdisciplinary in nature, drawing from both Perception and Virtual reality.
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.
