Understanding how attention varies across individuals has significant scientific and societal impacts. However, existing visual scanpath models treat attention uniformly, neglecting individual differences. To bridge this gap, this paper focuses on individualized scanpath prediction (ISP), a new attention modeling task that aims to accurately predict how different individuals shift their attention in diverse visual tasks. It proposes an ISP method featuring three novel technical components: (1) an observer encoder to characterize and integrate an observer's unique attention traits, (2) an observer-centric feature integration approach that holistically combines visual features, task guidance, and observer-specific characteristics, and (3) an adaptive fixation prioritization mechanism that refines scanpath predictions by dynamically prioritizing semantic feature maps based on individual observers' attention traits. These novel components allow scanpath models to effectively address the attention variations across different observers. Our method is generally applicable to different datasets, model architectures, and visual tasks, offering a comprehensive tool for transforming general scanpath models into individualized ones. Comprehensive evaluations using value-based and ranking-based metrics verify the method's effectiveness and generalizability.

翻译：理解注意力在不同个体间的差异具有重要的科学和社会影响。然而，现有的视觉扫视路径模型将注意力视为统一的，忽视了个体差异。为弥补这一不足，本文聚焦于个体化扫视路径预测（ISP），这是一个新的注意力建模任务，旨在准确预测不同个体在多样化视觉任务中如何转移注意力。本文提出了一种ISP方法，包含三个新颖技术组件：（1）一个观察者编码器，用于表征并整合观察者独特的注意力特征；（2）一种以观察者为中心的特征整合方法，整体结合视觉特征、任务指导和观察者特有特征；（3）一种自适应注视优先级机制，通过基于个体观察者的注意力特征动态优化语义特征图来改进扫视路径预测。这些新颖组件使扫视路径模型能够有效应对不同观察者间的注意力差异。我们的方法普遍适用于不同数据集、模型架构和视觉任务，为将通用扫视路径模型转化为个体化模型提供了全面工具。基于数值和排序指标的综合评估验证了该方法的有效性和泛化能力。