# Across-Game Engagement Modelling via Few-Shot Learning

arXiv: https://arxiv.org/abs/2409.13002 · HTML: https://mlsanity.com/paper/2409.13002/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2212.04500/) (2022)
- [Contrastive Learning for Prompt-Based Few-Shot Language Learners](https://mlsanity.com/paper/2205.01308/) (2022)
- [ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification](https://mlsanity.com/paper/2305.09269/) (2023)
- [From Pixels to Affect: A Study on Games and Player Experience](https://mlsanity.com/paper/1907.02288/) (2019)
- [From the Lab to the Wild: Affect Modeling via Privileged Information](https://mlsanity.com/paper/2305.10919/) (2023)
- [Towards General Models of Player Experience: A Study Within Genres](https://mlsanity.com/paper/2110.00978/) (2021)
- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions](https://mlsanity.com/paper/2310.06136/) (2023)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [The Ethics of AI in Games](https://mlsanity.com/paper/2305.07392/) (2023)
- [Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch](https://mlsanity.com/paper/2008.07207/) (2020)
- [RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks](https://mlsanity.com/paper/2204.06901/) (2022)
- [Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation](https://mlsanity.com/paper/2308.16029/) (2023)
- [GameVibe: A Multimodal Affective Game Corpus](https://mlsanity.com/paper/2407.12787/) (2024)

## Built on by (most cited first, 4 of 4)

- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [Emotions as Ambiguity-aware Ordinal Representations](https://mlsanity.com/paper/2508.19193/) (2025)
- [Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction](https://mlsanity.com/paper/2604.07198/) (2026)
- [Resource-Constrained Affect Modelling via Variance Regularisation Pruning](https://mlsanity.com/paper/2605.27479/) (2026)

## Abstract (arXiv)

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 across different games. Despite recent advancements in AI, domain generalisation for modelling the users' experience remains largely unexplored. While video games present unique challenges and opportunities for the analysis of user experience -- due to their dynamic and rich contextual nature -- modelling such experiences is limited by generally small datasets. As a result, conventional modelling methods often struggle to bridge the domain gap between users and games due to their reliance on large labelled training data and assumptions of common distributions of user experience. In this paper, we tackle this challenge by introducing a framework that decomposes the general domain-agnostic modelling of user experience into several domain-specific and game-dependent tasks that can be solved via few-shot learning. We test our framework on a variation of the publicly available GameVibe corpus, designed specifically to test a model's ability to predict user engagement across different first-person shooter games. Our findings demonstrate the superior performance of few-shot learners over traditional modelling methods and thus showcase the potential of few-shot learning for robust experience modelling in video games and beyond.