Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout
RGB-D semantic segmentation has achieved remarkable progress, yet most models assume that RGB and depth are always available. In practice, failures or occlusions of surveillance sensors often remove one modality. Although RGB or depth alone can contain sufficient cues, models trained only on full-modality inputs fail to exploit the remaining modality once one is missing, causing severe degradation. We tackle this issue with a simple continued-training paradigm, \emph{Condition Dropout (ConD)}, w
Record details
Published: 22 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Privacy
Retrieved: 23 July 2026
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ethics.ai (22 July 2026), “Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout,” evidence record 12956, https://ethics.ai/record/12956 (originally published by arXiv cs.AI).
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