activity
20192026
most citedThe Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

50 citations · 124 across the 17 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

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…

cs.CV2025

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…

cs.CV2024

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…

cs.CV2023

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…

cs.CV2022

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…

cs.CV2022

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,…