TDSal: Task-Based Top-Down Saliency Prediction Model
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.
| Authors | Can Mizrakli, Tolga K. Capin |
|---|---|
| Primary category | cs.CV |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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