Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to annotate and easy to overlook during active learning. Standard acquisition heuristics based on discriminative uncertainty or feature diversity often overselect dominant patterns while underexploring sparse yet important regions of the data space. This failure mode is especially severe in industrial defect inspection, where anomalies may be both low-prevalence and difficult to distinguish from surrounding structure. To resolve this, we propose GSAL, an active learning framework for object detection that combines a...
| Authors | Renjith Prasad, Rishabh Sharma, Andrew E. Shao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 28 April 2026 |
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