Alfa: 用于结构感知跨域个性化眼动估计的注意力低秩滤波适应
Alfa: Attentive Low-Rank Filter Adaptation for Structure-Aware Cross-Domain Personalized Gaze Estimation
AI总结：
Alfa通过重新加权预训练滤波器中的语义模式，实现结构感知的跨域个性化眼动估计，有效提升模型性能。
摘要：
预训练的眼动模型学习识别在用户间普遍存在的有用模式，但细微的用户特定变化（即眼睑形状或面部结构）可能会降低模型性能。测试时个性化（TTP）利用少量未标记样本将预训练模型适应到这些用户特定的域偏移。高效的微调对于执行这种域适应至关重要：数据和计算资源可能有限，特别是对于设备上的定制。虽然流行的参数高效微调（PEFT）方法通过仅更新一小部分权重来解决适应成本，但它们可能没有充分利用预训练滤波器中编码的结构。为了更有效地利用预训练期间学习到的结构，我们将个性化重新表述为一个过程，即重新加权现有特征，而不是学习全新的特征。我们提出了注意力低秩滤波适应（Alfa）来通过重新加权预训练滤波器中的语义模式来适应眼动模型。通过SVD提取出的奇异值分解（SVD）提取出主导的空间组件，这些组件捕捉了用户之间的眼部和面部特征。通过注意力机制，我们只需要少量未标记样本来调整和重新加权预训练结构，选择性地放大那些与目标用户相关的内容。Alfa在四个跨数据集眼动基准测试中实现了最低的平均眼动误差，优于现有的TTP方法和基于低秩适应（LoRA）的变体。我们还展示了Alfa的注意力低秩方法可以应用于除视觉之外的其他应用，如基于扩散的语言模型。
英文摘要：
Pre-trained gaze models learn to identify useful patterns commonly found across users, but subtle user-specific variations (i.e., eyelid shape or facial structure) can degrade model performance. Test-time personalization (TTP) adapts pre-trained models to these user-specific domain shifts using only a few unlabeled samples. Efficient fine-tuning is critical in performing this domain adaptation: data and computation resources can be limited-especially for on-device customization. While popular parameter-efficient fine-tuning (PEFT) methods address adaptation costs by updating only a small set of weights, they may not be taking full advantage of structures encoded in pre-trained filters. To more effectively leverage existing structures learned during pre-training, we reframe personalization as a process to reweight existing features rather than learning entirely new ones. We present Attentive Low-Rank Filter Adaptation (Alfa) to adapt gaze models by reweighting semantic patterns in pre-trained filters. With Alfa, singular value decomposition (SVD) extracts dominant spatial components that capture eye and facial characteristics across users. Via an attention mechanism, we need only a few unlabeled samples to adjust and reweight pre-trained structures, selectively amplifying those relevant to a target user. Alfa achieves the lowest average gaze errors across four cross-dataset gaze benchmarks, outperforming existing TTP methods and low-rank adaptation (LoRA)-based variants. We also show that Alfa's attentive low-rank methods can be applied to applications beyond vision, such as diffusion-based language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
