What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Robustness.
His Artificial intelligence study often links to related topics such as Set.
His work carried out in the field of Pattern recognition brings together such families of science as Histogram, Field and Visualization.
The concepts of his Machine learning study are interwoven with issues in Task, Crowd psychology and Signal processing.
His Robustness study incorporates themes from Social relation, Video tracking, BitTorrent tracker and Social robot.
His Feature extraction research includes elements of Contextual image classification, Speech recognition and Constraint.
His most cited work include:
- Person re-identification by symmetry-driven accumulation of local features (1295 citations)
- Custom Pictorial Structures for Re-identification (486 citations)
- Synthesis of unequally spaced arrays by simulated annealing (290 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Segmentation.
His studies in Feature extraction, Support vector machine, Pattern recognition, Representation and Image segmentation are all subfields of Artificial intelligence research.
Vittorio Murino frequently studies issues relating to Contextual image classification and Feature extraction.
His Computer vision study frequently draws parallels with other fields, such as Robustness.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Feature, Generative model and Cluster analysis.
His Machine learning study combines topics in areas such as Inference and Benchmark.
He most often published in these fields:
- Artificial intelligence (91.21%)
- Computer vision (33.00%)
- Pattern recognition (34.66%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (91.21%)
- Machine learning (29.19%)
- Pattern recognition (34.66%)
In recent papers he was focusing on the following fields of study:
Vittorio Murino mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Segmentation.
His study looks at the intersection of Artificial intelligence and topics like Computer vision with Optimal matching.
Vittorio Murino has researched Machine learning in several fields, including Generalization and Pattern recognition.
His Feature extraction study in the realm of Pattern recognition connects with subjects such as Domain adaptation.
His study focuses on the intersection of Deep learning and fields such as Supervised learning with connections in the field of Training set.
His work on Multiple sclerosis lesion is typically connected to Vulnerability as part of general Segmentation study, connecting several disciplines of science.
Between 2016 and 2021, his most popular works were:
- Generalizing to Unseen Domains via Adversarial Data Augmentation (135 citations)
- Adversarial Feature Augmentation for Unsupervised Domain Adaptation (126 citations)
- Modality Distillation with Multiple Stream Networks for Action Recognition (70 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Deep learning.
His Computer vision research extends to Artificial intelligence, which is thematically connected.
His work in the fields of Particle filter overlaps with other areas such as Spectral signature.
His Pattern recognition research incorporates themes from Video tracking and Scale.
His Feature learning study, which is part of a larger body of work in Machine learning, is frequently linked to Action recognition, bridging the gap between disciplines.
His work deals with themes such as Regularization, Data mining and Re identification, which intersect with Segmentation.
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.
