Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on task-agnostic saliency models, although visual attention is inherently task-dependent. Collecting task-relevant importance data remains a resource-intensive challenge. To address this, we introduce Grid Labeling, a novel annotation method for collecting task-specific importance data to enhance saliency prediction models. Grid Labeling dynamically segments visualizations into Adaptive Grids, enabling efficient, low-effort annotation while adapting to visualization structure. We conducted a human-subject study comparing Grid Labeling with existing annotation methods, ImportAnnots, and BubbleView across multiple metrics. Results show that Grid Labeling produces the least noisy data and the highest inter-participant agreement with fewer participants while requiring less physical (e.g., clicks or mouse movements) and cognitive effort.

@inproceedings{chang25_eurovis,
  title = {Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations},
  author = {Minsuk Chang and Yao Wang and Huichen Will Wang and Andreas Bulling and Cindy Xiong Bearfield},
  year = {2025},
  booktitle = {Proc. 27th Annual Conference on Data Visualization (EuroVis)},
  pages = {1--6},
  doi = {10.2312/evs.20251092},
}