What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Anthony Dick mainly focuses on Artificial intelligence, Computer vision, Question answering, Recurrent neural network and Image.
His research brings together the fields of Machine learning and Artificial intelligence.
Anthony Dick interconnects Computer graphics, Robustness and Pattern recognition in the investigation of issues within Computer vision.
His research in Question answering intersects with topics in Knowledge extraction and Natural language.
His Recurrent neural network research is multidisciplinary, incorporating perspectives in Natural language processing, Convolutional neural network, Feature and Closed captioning.
His work on 2d images as part of general Image research is often related to Tracing and Series, thus linking different fields of science.
His most cited work include:
- A survey of appearance models in visual object tracking (575 citations)
- What Value Do Explicit High Level Concepts Have in Vision to Language Problems (311 citations)
- Online multi-target tracking using recurrent neural networks (255 citations)
What are the main themes of his work throughout his whole career to date?
Anthony Dick spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image and Question answering.
He has included themes like Machine learning and Natural language processing in his Artificial intelligence study.
His work on Pixel, Active appearance model, Structure from motion and Segmentation is typically connected to Process as part of general Computer vision study, connecting several disciplines of science.
His Pattern recognition study integrates concerns from other disciplines, such as Object detection, Eye tracking and Robustness.
His research integrates issues of Recurrent neural network, Representation and Natural language in his study of Question answering.
As a member of one scientific family, he mostly works in the field of Video tracking, focusing on Discriminative model and, on occasion, Similarity measure.
He most often published in these fields:
- Artificial intelligence (79.70%)
- Computer vision (45.86%)
- Pattern recognition (21.05%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (79.70%)
- Question answering (14.29%)
- Recurrent neural network (10.53%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Question answering, Recurrent neural network, Natural language processing and Deep learning.
The Artificial intelligence study combines topics in areas such as Machine learning, State and Pattern recognition.
Anthony Dick has included themes like Tracking and Multi target tracking in his Machine learning study.
Anthony Dick combines subjects such as Image, Representation and Natural language with his study of Question answering.
His research in Recurrent neural network focuses on subjects like Convolutional neural network, which are connected to Range.
His work deals with themes such as Semantics and Relevance, which intersect with Natural language processing.
Between 2015 and 2021, his most popular works were:
- What Value Do Explicit High Level Concepts Have in Vision to Language Problems (311 citations)
- Online multi-target tracking using recurrent neural networks (255 citations)
- Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources (232 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Anthony Dick mostly deals with Artificial intelligence, Question answering, Recurrent neural network, Natural language and Knowledge base.
Artificial intelligence is closely attributed to Natural language processing in his study.
The various areas that Anthony Dick examines in his Recurrent neural network study include Convolutional neural network, Feature and Closed captioning.
His Knowledge extraction research is multidisciplinary, relying on both Visualization, Information retrieval and Knowledge-based systems.
His Tracking research is multidisciplinary, incorporating perspectives in Machine learning, Deep learning and State.
His Image study integrates concerns from other disciplines, such as Representation, Protocol and Explicit knowledge.
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