TDSal: Task-Based Top-Down Saliency Prediction Model
Can Mizrakli, Tolga K. Capin
cs.CV
Abstract
Visual saliency aims to predict the regions of an image most likely to attract human visual attention. While most saliency models assume free-viewing conditions, human attention is often shaped by explicit task goals. In this work, we address task-driven saliency prediction by proposing a model that conditions visual attention on natural-language task descriptions. The model produces task-dependent saliency maps that reflect how attention shifts under different viewing intents. Through quantitative and qualitative analysis, we show that incorporating explicit task semantics enables more faithful modeling of goal-directed visual attention.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.