18 citations · 18 across the 3 of their papers we have counts for
9 papers
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…
Learning using privileged information for segmenting tumors on digital mammograms
Ioannis N. Tzortzis, Konstantinos Makantasis, Ioannis Rallis +3
Limited amount of data and data sharing restrictions, due to GDPR compliance, constitute two common factors leading to reduced availability and accessibility when referring to medi…
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…
Towards General Game Representations: Decomposing Games Pixels into Content and Style
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
On-screen game footage contains rich contextual information that players process when playing and experiencing a game. Learning pixel representations of games can benefit artificia…
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…