23 citations · 23 across the 6 of their papers we have counts for
7 papers
SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video
Marija Jegorova, Stavros Petridis, Maja Pantic
This work focuses on the apparent emotional reaction recognition (AERR) from the video-only input, conducted in a self-supervised fashion. The network is first pre-trained on diffe…
Survey: Leakage and Privacy at Inference Time
Marija Jegorova, Chaitanya Kaul, Charlie Mayor +4
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources…
Similarity-based data mining for online domain adaptation of a sonar ATR system
Jean de Bodinat, Thomas Guerneve, Jose Vazquez +1
Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often add…
Unlimited Resolution Image Generation with R2D2-GANs
Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez +1
In this paper we present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equ…
Adversarial Generation of Informative Trajectories for Dynamics System Identification
Marija Jegorova, Joshua Smith, Michael Mistry +1
Dynamic System Identification approaches usually heavily rely on the evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determin…
Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks
Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez +1
Deployment and operation of autonomous underwater vehicles is expensive and time-consuming. High-quality realistic sonar data simulation could be of benefit to multiple application…
Behavioural Repertoire via Generative Adversarial Policy Networks
Marija Jegorova, Stéphane Doncieux, Timothy Hospedales
Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpect…