no code implementations • 26 Oct 2022 • Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu, Kalliopi Basioti, Dhaivat J. Bhatt, Vladimir Pavlovic, Afsaneh Fazly
The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e. g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations.
no code implementations • 18 Jun 2022 • Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic
We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM).
no code implementations • 27 Sep 2021 • Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic
Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts used in the test (i. e., the representation) as well as the latent rules based on those concepts (i. e., the reasoning).
no code implementations • 1 Jan 2021 • Kalliopi Basioti, George V. Moustakides
We are interested in the design of generative networks.
no code implementations • 15 Jul 2020 • Kalliopi Basioti, George V. Moustakides
Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size.
no code implementations • 27 May 2020 • Kalliopi Basioti, George V. Moustakides
When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc.
no code implementations • 3 Feb 2020 • Kalliopi Basioti, George V. Moustakides
We are interested in the design of generative networks.
no code implementations • 1 Nov 2019 • George V. Moustakides, Kalliopi Basioti
In classical approaches the two densities are assumed known or to belong to some known parametric family.
no code implementations • 24 May 2019 • Kalliopi Basioti, George V. Moustakides
Data driven classification that relies on neural networks is based on optimization criteria that involve some form of distance between the output of the network and the desired label.
no code implementations • 23 Nov 2018 • Kalliopi Basioti, George V. Moustakides, Emmanouil Z. Psarakis
Generative adversarial networks (GANs) are designed with the help of min-max optimization problems that are solved with stochastic gradient-type algorithms which are known to be non-robust.