In this work, we take the first step in the direction of a bottom-up self-supervised approach in the domain.
1 code implementation • 1 Dec 2020 • Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation.
In this work, we propose a modular and cost-effective wireless approach for synchronized multisensor data acquisition of social human behavior.
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user.
In this paper, we investigate the use of proxemics and dynamics for automatically identifying conversing groups, or so-called F-formations.
We motivate a metric for the existence of distinct conversation floors based on simultaneous speaker turns, and provide an analysis using this metric to characterize conversations across F-formations of varying cardinality.
As well as fitting a more flexible model to missing labels in time, we posit that our approach also loosens the head and body coupling constraint, allowing for a more expressive model of the head and body pose typically seen during conversational interaction in groups.
In this paper, we present the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images.