What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary areas of investigation include Artificial intelligence, Natural language, Natural language processing, Image and Question answering.
His research links Machine learning with Artificial intelligence.
Convolutional neural network is closely connected to Deep learning in his research, which is encompassed under the umbrella topic of Natural language.
In general Natural language processing study, his work on Language identification often relates to the realm of Referring expression and Ground, thereby connecting several areas of interest.
The study incorporates disciplines such as Representation and Pooling in addition to Question answering.
Marcus Rohrbach combines subjects such as Interpretation and Benchmark with his study of Training set.
His most cited work include:
- Long-term recurrent convolutional networks for visual recognition and description (3486 citations)
- Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding (830 citations)
- Sequence to Sequence -- Video to Text (747 citations)
What are the main themes of his work throughout his whole career to date?
Marcus Rohrbach mostly deals with Artificial intelligence, Natural language processing, Machine learning, Natural language and Question answering.
His research ties Pattern recognition and Artificial intelligence together.
The Natural language processing study combines topics in areas such as Visualization and Resolution.
His work on Artificial neural network and Bayesian neural networks as part of general Machine learning research is frequently linked to Key, Process and Protocol, thereby connecting diverse disciplines of science.
Marcus Rohrbach has researched Natural language in several fields, including Object, Recurrent neural network, Deep learning and Benchmark.
In Question answering, Marcus Rohrbach works on issues like Set, which are connected to Turing test.
He most often published in these fields:
- Artificial intelligence (97.12%)
- Natural language processing (41.73%)
- Machine learning (30.22%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (97.12%)
- Machine learning (30.22%)
- Image (25.90%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Image, Code and Context are his primary areas of study.
His study ties his expertise on Natural language processing together with the subject of Artificial intelligence.
His studies deal with areas such as Optical character recognition and Coreference as well as Natural language processing.
His research integrates issues of Question answering, Isolation, Set, Visualization and Image retrieval in his study of Machine learning.
His studies examine the connections between Image and genetics, as well as such issues in Benchmark, with regards to Pattern recognition, Relation and Object.
His Closed captioning research integrates issues from Sentence, Visual reasoning and Minimum bounding box.
Between 2018 and 2021, his most popular works were:
- Graph-Based Global Reasoning Networks (150 citations)
- Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution (120 citations)
- Towards VQA Models That Can Read (108 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Convolutional neural network, Machine learning, Class and Task.
His research related to Benchmark, Feature learning, Deep learning, Classifier and Discriminative model might be considered part of Artificial intelligence.
His Benchmark research incorporates elements of Class, Object, Image and Information retrieval.
His Convolutional neural network research focuses on Image resolution and how it relates to Redundancy, Kernel and Feature extraction.
His biological study deals with issues like Question answering, which deal with fields such as Question generation, Consistency and Set.
Marcus Rohrbach interconnects Knowledge transfer and Image retrieval in the investigation of issues within Task.
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