  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.
