From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted annotation has therefore become standard in large-scale labeling workflows. However, in tasks where model predictions carry two independent components, a class label and spatial boundaries, a model may classify an object with high confidence while mislocalizing it. Existing AI-assisted workflows offer annotators no signal about where spatial errors are most likely. Without such guidance, humans may systematically underinspect subtly misplaced boxes. We address this by studying the effect of visualizing spatial uncertainty via a purpose-built...
| Authors | Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer et al. |
|---|---|
| Primary category | cs.HC |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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