50 citations · 124 across the 17 of their papers we have counts for
20 papers
Causal Supervision of Attention for Affective Behaviour Analysis
Nemanja Rašajski, Konstantinos Makantasis, Antonios Liapis +1
The \textit{11th Affective Behaviour Analysis in-the-wild Competition} includes the Multi-Task Learning Challenge, where participants develop a unified framework for Valence-Arousa…
Learning Using Privileged Information for Litter Detection
Matthias Bartolo, Konstantinos Makantasis, Dylan Seychell
As litter pollution continues to rise globally, developing automated tools capable of detecting litter effectively remains a significant challenge. This study presents a novel appr…
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…
BehAVE: Behaviour Alignment of Video Game Encodings
Nemanja Rašajski, Chintan Trivedi, Konstantinos Makantasis +2
Domain randomisation enhances the transferability of vision models across visually distinct domains with similar content. However, current methods heavily depend on intricate simul…
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…
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…
Game State Learning via Game Scene Augmentation
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural conte…
Revisiting lp-constrained Softmax Loss: A Comprehensive Study
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large. The impact of particular forms of norma…
Learning Task-Independent Game State Representations from Unlabeled Images
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Self-supervised learning (SSL) techniques have been widely used to learn compact and informative representations from high-dimensional complex data. In many computer vision tasks,…
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…
AffRankNet+: Ranking Affect Using Privileged Information
Konstantinos Makantasis
Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which…
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…