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cs.CV2026
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
Galadrielle Humblot-Renaux, Vasiliki Ismiroglou, Malte Pedersen
Label uncertainty and annotator disagreement are common challenges in the field of computer vision, yet their study has largely been confined to the medical domain or to generic im…
cs.CV2026
Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes
Vasiliki Ismiroglou, Stefan H. Bengtson, Tasos Benos +2
Visibility in underwater environments degrades rapidly under turbid conditions, yet the effects on computer-vision models remain unclear. This issue is compounded by reliance on sy…