20 citations · 74 across the 14 of their papers we have counts for
14 papers
The Ordinal Annotation Game: How Construct Abstraction Shapes Crowdsourced Consensus
Kosmas Pinitas
Inter-annotator disagreement in real-time affect annotation is widely treated as stochastic noise. We challenge this view by modelling ordinal annotation as an implicit game-theore…
Resource-Constrained Affect Modelling via Variance Regularisation Pruning
Kosmas Pinitas, Konstantinos Katsifis
Affective computing systems are increasingly embedded in pervasive and interactive environments, such as adaptive games, assistive technologies, and resource-constrained platforms,…
Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction
Kosmas Pinitas, Ilias Maglogiannis
Emotion annotation is inherently subjective and cognitively demanding, producing signals that reflect diverse perceptions across annotators rather than a single ground truth. In co…
LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics
Kosmas Pinitas, Ilias Maglogiannis
Predicting affect in unconstrained environments remains a fundamental challenge in human-centered AI. While deep neural embeddings dominate contemporary approaches, they often lack…
Privileged Contrastive Pretraining for Multimodal Affect Modelling
Kosmas Pinitas, Konstantinos Makantasis, Georgios N. Yannakakis
Affective Computing (AC) has made significant progress with the advent of deep learning, yet a persistent challenge remains: the reliable transfer of affective models from controll…
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…
The Invariant Ground Truth of Affect
Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis +1
Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is p…
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…
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis +1
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events…
Dendritic Self-Organizing Maps for Continual Learning
Kosmas Pinitas, Spyridon Chavlis, Panayiota Poirazi
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets. However, the predictive ability of these architectures significant…