Task-driven Webpage Saliency - quanlzheng.github.io · When visiting a webpage, people look at...
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Task-driven Webpage Saliency 1 1 Quanlong Zheng Jianbo Jiao Ying Cao Rynson W.H. Lau 1 1, 2 City University of Hong Kong 1 University of Illinois at Urbana-Champaign 2 Contribution s • A new benchmark dataset for the evaluation of webpage saliency prediction under multi-task condition. • We address webpage saliency prediction under multi-task condition. • We propose a disentangled framework which can be efficiently trained from a small-scale task- driven saliency dataset with sparse annotations. Component saliency ratio for each semantic component (column) under each task (row). This table shows that human attention on webpages under the task-driven condition is related to the semantic components of webpages. Data analysis Results Motivation When visiting a webpage, people look at different places under different tasks. Architecture We propose to disentangle task-driven saliency prediction into two sub-tasks: task-specific attention shift prediction and task-free saliency prediction. Benchmark Dataset: 200 webpages of four categories (shopping, traveling, games and email) , each of which has eye gaze data under a single or multiple tasks.
Transcript of Task-driven Webpage Saliency - quanlzheng.github.io · When visiting a webpage, people look at...
Task-driven Webpage Saliency1 1Quanlong Zheng Jianbo Jiao Ying Cao Rynson W.H. Lau1 1, 2
City University of Hong Kong 1 University of Illinois at Urbana-Champaign2
Contributions
•A new benchmark dataset for the evaluation of webpage saliency prediction under multi-task condition.
•We address webpage saliency prediction under multi-task condition.
•We propose a disentangled framework which canbe efficiently trained from a small-scale task-driven saliency dataset with sparse annotations.
Component saliency ratio for each semantic component(column) under each task (row). This table shows thathuman attention on webpages under the task-drivencondition is related to the semantic components ofwebpages.
Data analysis
Results
Motivation
When visiting a webpage, people look at different places under different tasks.
Architecture
We propose to disentangle task-driven saliency prediction into two sub-tasks: task-specific attention shift prediction and task-free saliency prediction.
Benchmark Dataset: 200 webpages of four categories (shopping, traveling, games and email) , each of which has eye gaze data under a single or multiple tasks.
