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Clinical Risk Prediction Using Language Models: Benefits And Considerations
Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods.
Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models
We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks.
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene
Given the spatial and temporal consistency cues used for pixel level data association, we use a variant of contrastive learning to train a DCNN model for predicting semantic segmentation from RGB views in the target environment.
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