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Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage — arXiv:2610.07603

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07603
License (as archived): CC0-1.0
Archived: 2026-10-07T11:20:55.082Z
SHA-256 of the archived text: 918c1d3f661ddca61dec5afb7c66bdbddbad15a84a3eb086719092c218a03646
Stable URL of this page: https://metraipolis.com/documento/dc_682da8cd795c

How to cite

Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage — arXiv:2610.07603 — arXiv (en), https://arxiv.org/abs/2610.07603 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_682da8cd795c (Metraipolis archive dc_682da8cd795c, sha256 918c1d3f661ddca61dec5afb7c66bdbddbad15a84a3eb086719092c218a03646)

BibTeX

@misc{dc682da8cd795c,
  title = {Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage — arXiv:2610.07603},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07603},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_682da8cd795c; sha256 918c1d3f661ddca61dec5afb7c66bdbddbad15a84a3eb086719092c218a03646},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07603. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This article proposes Selective Affective Layer Fine-Tuning (SALFT), an efficient adaptation framework for Video Vision Transformers in player arousal recognition from gameplay. To bypass computationally expensive full fine-tuning, SALFT introduces a selection criterion based on the L2-norm change in layer parameters after brief adaptation, directly measuring representational shifts and providing a more stable basis than gradient-based alternatives. Evaluated via five-fold cross-validation on the Arousal Video Game AnnotatIoN dataset, SALFT achieves performance comparable to full fine-tuning across all games without statistically significant degradation ($p>0.05$), while updating only $\approx$8% of parameters (over 92% reduction). Notably, in one game, SALFT consistently outperforms both full fine-tuning and the best baseline across all metrics and folds, reaching the theoretical minimum p-value (p=0.0625, exact two-sided Wilcoxon signed-rank test). In addition, we introduce an interpretability method to trace attention patterns, enhancing model transparency. These results establish SALFT as an effective and efficient approach for affective game computing.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.07603 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:24.739Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).