Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout

Researchers have made significant strides in RGB-D semantic segmentation, but most models rely on the simultaneous availability of RGB and depth data. However, in real-world applications, sensor failures or occlusions can lead to the loss of one modality, causing model performance to degrade severely. To address this issue, a new approach utilizes condition dropout to handle missing modalities, enabling models to effectively exploit the remaining modality when one is absent¹. This is crucial, as RGB or depth data alone can often provide sufficient cues for accurate segmentation. By developing more robust models, researchers can improve the reliability of RGB-D semantic segmentation in various applications, including surveillance and autonomous systems. This advancement has significant implications for fields beyond technology, including policy, security, and workforce dynamics, as AI systems become increasingly integrated into everyday life.