14 citations · 22 across the 10 of their papers we have counts for
11 papers
GenVP: Generating Visual Puzzles with Contrastive Hierarchical VAEs
Kalliopi Basioti, Pritish Sahu, Qingze Tony Liu +3
Raven's Progressive Matrices (RPMs) is an established benchmark to examine the ability to perform high-level abstract visual reasoning (AVR). Despite the current success of algorit…
Box2Flow: Instance-based Action Flow Graphs from Videos
Jiatong Li, Kalliopi Basioti, Vladimir Pavlovic
A large amount of procedural videos on the web show how to complete various tasks. These tasks can often be accomplished in different ways and step orderings, with some steps able…
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation
Kalliopi Basioti, Mohamed A. Abdelsalam, Federico Fancellu +2
Controllable Image Captioning (CIC) aims at generating natural language descriptions for an image, conditioned on information provided by end users, e.g., regions, entities or even…
Visual Semantic Parsing: From Images to Abstract Meaning Representation
Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu +4
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…
SAViR-T: Spatially Attentive Visual Reasoning with Transformers
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). Our model considers explicit spat…
Image De-Quantization Using Generative Models as Priors
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. De-quantization is the task of reversing th…
Image Restoration from Parametric Transformations using Generative Models
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-…
Training Neural Networks for Likelihood/Density Ratio Estimation
George V. Moustakides, Kalliopi Basioti
Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are…
Optimizing Shallow Networks for Binary Classification
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 lab…
Adaptive Blind Separation of Two Dependent Sources
George V. Moustakides, Feeby Salib, Kalliopi Basioti
We consider the problem of adaptive blind separation of two sources from their instantaneous mixtures. We focus on the case where the two sources are not necessarily independent. B…
Kernel-Based Training of Generative Networks
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 no…