50 citations · 124 across the 17 of their papers we have counts for
9 papers · 1 filter
Across-Game Engagement Modelling via Few-Shot Learning
Kosmas Pinitas, Konstantinos Makantasis, Georgios N. Yannakakis
Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for in…
GameVibe: A Multimodal Affective Game Corpus
Matthew Barthet, Maria Kaselimi, Kosmas Pinitas +3
As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, th…
Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions
Kosmas Pinitas, David Renaudie, Mike Thomsen +4
This paper introduces a large scale multimodal corpus collected for the purpose of analysing and predicting player engagement in commercial-standard games. The corpus is solicited…
Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation
Matthew Barthet, Chintan Trivedi, Kosmas Pinitas +4
The laborious and costly nature of affect annotation is a key detrimental factor for obtaining large scale corpora with valid and reliable affect labels. Motivated by the lack of t…
From the Lab to the Wild: Affect Modeling via Privileged Information
Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis +1
How can we reliably transfer affect models trained in controlled laboratory conditions (in-vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitr…
Supervised Contrastive Learning for Affect Modelling
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis +1
Affect modeling is viewed, traditionally, as the process of mapping measurable affect manifestations from multiple modalities of user input to affect labels. That mapping is usuall…
Privileged Information for Modeling Affect In The Wild
Konstantinos Makantasis, David Melhart, Antonios Liapis +1
A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild.…
The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games
Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis
What if emotion could be captured in a general and subject-agnostic fashion? Is it possible, for instance, to design general-purpose representations that detect affect solely from…
From Pixels to Affect: A Study on Games and Player Experience
Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis
Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely l…