What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Random forest.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Computation and Metric.
Many of his research projects under Pattern recognition are closely connected to Domain adaptation with Domain adaptation, tying the diverse disciplines of science together.
His research in Deep learning tackles topics such as Categorization which are related to areas like Segmentation, Computer vision and Pyramid.
His research integrates issues of Shape matching, Small set and Heat kernel signature in his study of Random forest.
His studies examine the connections between Visualization and genetics, as well as such issues in Image resolution, with regards to Object.
His most cited work include:
- The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes (482 citations)
- Deep Neural Decision Forests (221 citations)
- In-place Activated BatchNorm for Memory-Optimized Training of DNNs (188 citations)
What are the main themes of his work throughout his whole career to date?
Samuel Rota Bulò mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Cluster analysis and Deep learning.
His research brings together the fields of Computer vision and Artificial intelligence.
His work on Class as part of general Pattern recognition study is frequently linked to Domain adaptation and Transformation, therefore connecting diverse disciplines of science.
In his research on the topic of Machine learning, Contextual image classification is strongly related with Categorization.
His biological study spans a wide range of topics, including Theoretical computer science and Data mining.
His work in Deep learning addresses issues such as Normalization, which are connected to fields such as Computation.
He most often published in these fields:
- Artificial intelligence (72.73%)
- Pattern recognition (29.75%)
- Machine learning (23.97%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.73%)
- Pattern recognition (29.75%)
- Segmentation (12.40%)
In recent papers he was focusing on the following fields of study:
Samuel Rota Bulò spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Benchmark and Object detection.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His Pattern recognition research incorporates elements of Video tracking, Feature and Task.
His Segmentation study integrates concerns from other disciplines, such as Annotation and Evolutionary game theory.
His Object detection research incorporates themes from Ranking, Monocular, Robustness and Test set.
His Deep learning research includes elements of Embedding, Normalization and Image segmentation.
Between 2019 and 2021, his most popular works were:
- Modeling the Background for Incremental Learning in Semantic Segmentation (25 citations)
- Learning Multi-Object Tracking and Segmentation From Automatic Annotations (14 citations)
- Inferring Latent Domains for Unsupervised Deep Domain Adaptation (10 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, Pattern recognition, Object detection, Monocular and Image segmentation.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His research in Machine learning intersects with topics in Classifier and Pixel.
His Object detection research incorporates themes from Stereo camera and Benchmark.
His research integrates issues of Object and Visual appearance in his study of Monocular.
His Training set study combines topics in areas such as Heuristic, Cluster analysis, Boosting, Robot and Convolutional neural network.
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