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Gianfranco Doretto
Researcher at West Virginia University
Publications - 91
Citations - 5617
Gianfranco Doretto is an academic researcher from West Virginia University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 29, co-authored 80 publications receiving 4780 citations. Previous affiliations of Gianfranco Doretto include University of California, Los Angeles & NBCUniversal (United States).
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Journal ArticleDOI
Dynamic Textures
TL;DR: A characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing and experimental evidence that, within the framework, even low-dimensional models can capture very complex visual phenomena is presented.
Proceedings ArticleDOI
Unified Deep Supervised Domain Adaptation and Generalization
TL;DR: This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models by reverting to point-wise surrogates of distribution distances and similarities by exploiting the Siamese architecture.
Proceedings ArticleDOI
Shape and Appearance Context Modeling
TL;DR: This work develops appearance models for computing the similarity between image regions containing deformable objects of a given class in realtime, and introduces the concept of shape and appearance context.
Proceedings ArticleDOI
Boosting for transfer learning with multiple sources
Yi Yao,Gianfranco Doretto +1 more
TL;DR: Two new algorithms, MultiSource-TrAdaBoost, and TaskTrAdABoost, are introduced, analyzed, and applied for object category recognition and specific object detection and demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases.
Proceedings ArticleDOI
Dynamic texture recognition
TL;DR: This work poses the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented and examines three different distances in thespace of autoregressive models and assess their power.