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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 23·5 min read
Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout
TL;DR
Condition Dropout (ConD) enhances RGB-D semantic segmentation by addressing the issue of missing modalities, improving model robustness and accuracy. This method allows models to better utilize available data when either RGB or depth information is absent.
✦ Why It Matters
Implement Condition Dropout in your RGB-D models to enhance robustness against missing data.
Key Takeaways
- Adopt Condition Dropout to enhance model performance with incomplete data.
- Train models with simulated missing RGB or depth inputs for better robustness.
- Utilize zero-initialized feature injection to improve training outcomes.
- Evaluate model performance on benchmark datasets like NYU-Depth V2 and SUN RGB-D.
How It Works
Condition Dropout (ConD) operates by first freezing the original encoders of a pretrained RGB-D model. It then introduces a second training phase where the model is exposed to a mix of complete, RGB-missing, and depth-missing inputs.
This approach allows the model to learn how to leverage the available modality effectively, even when one is absent, thus enhancing its overall robustness.