发表机构
Ritsumeikan University; Carnegie Mellon University（立命馆大学; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SALFT，一种基于层参数L2范数变化选择情感层微调视频视觉Transformer的方法，用于从游戏画面识别玩家唤醒度变化，仅更新约8%参数即达到与全量微调相当的性能，并引入注意力追踪可解释性方法。
AI 中文摘要
本文提出了选择性情感层微调（Selective Affective Layer Fine-Tuning, SALFT），一种用于视频视觉Transformer在玩家唤醒度识别中的高效适配框架。为绕过计算成本高昂的全量微调，SALFT引入了一种基于短暂适配后层参数L2范数变化的选择准则，直接度量表征偏移，相比基于梯度的方法提供了更稳定的基础。通过在Arousal Video Game AnnotatIoN数据集上进行五折交叉验证评估，SALFT在所有游戏中的性能与全量微调相当，无统计学显著退化（p>0.05），同时仅更新约8%的参数（减少超过92%）。值得注意的是，在某一款游戏中，SALFT在所有指标和折上持续优于全量微调和最佳基线，达到理论最小p值（p=0.0625，精确双侧Wilcoxon符号秩检验）。此外，我们引入了一种可解释性方法来追踪注意力模式，增强模型透明度。这些结果确立了SALFT作为情感游戏计算中一种有效且高效的方法。
英文摘要
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.
Journal refIEEE Transactions on Games, vol. 18, no. 2, pp. 393-403, June 2026
