Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Makantasis, Konstantinos"'
Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for instance, such AI models can supposedly learn to detect player actions a
Externí odkaz:
http://arxiv.org/abs/2409.13002
Autor:
Barthet, Matthew, Kaselimi, Maria, Pinitas, Kosmas, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality au
Externí odkaz:
http://arxiv.org/abs/2407.12787
Autor:
Tzortzis, Ioannis N., Makantasis, Konstantinos, Rallis, Ioannis, Bakalos, Nikolaos, Doulamis, Anastasios, Doulamis, Nikolaos
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 medical data. To tackle these issues, we introduce the technique of Learni
Externí odkaz:
http://arxiv.org/abs/2402.06379
Autor:
Trivedi, Chintan, Rašajski, Nemanja, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
Domain randomization is an effective computer vision technique for improving transferability of vision models across visually distinct domains exhibiting similar content. Existing approaches, however, rely extensively on tweaking complex and speciali
Externí odkaz:
http://arxiv.org/abs/2402.01335
Autor:
Pinitas, Kosmas, Renaudie, David, Thomsen, Mike, Barthet, Matthew, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
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 from 25 players of the action role-playing game Tom Clancy's The Divis
Externí odkaz:
http://arxiv.org/abs/2310.06136
Autor:
Barthet, Matthew, Trivedi, Chintan, Pinitas, Kosmas, Xylakis, Emmanouil, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
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 tools that can effectively determine an annotator's reliability, this p
Externí odkaz:
http://arxiv.org/abs/2308.16029
On-screen game footage contains rich contextual information that players process when playing and experiencing a game. Learning pixel representations of games can benefit artificial intelligence across several downstream tasks including game-playing
Externí odkaz:
http://arxiv.org/abs/2307.11141
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-vitro and in-vivo applications defines a core challenge of affective compu
Externí odkaz:
http://arxiv.org/abs/2305.10919
Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is predominately attributed to the affect labels which inadvertently inclu
Externí odkaz:
http://arxiv.org/abs/2210.07630
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 usually inferred through end-to-end (manifestation-to-affect) machine learni
Externí odkaz:
http://arxiv.org/abs/2208.12238