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)}, which mitigates degradation while preserving full-modality accuracy. Starting from a pretrained RGB-D
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- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Toward Reliable RGB-D Semantic Segmentation: Handling Missin” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Xuchen Zhu →
“Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout”
- LinkedLinked via arxiv author · 85%Yajuan Wei →
“Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout”
- LinkedLinked via arxiv author · 85%Shuang Hao →
“Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout”
- LinkedLinked via arxiv author · 85%Jiwei Jiang →
“Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout”
- LinkedLinked via arxiv author · 85%Guanxiang Mao →
“Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout”