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Found 6 papers, 5 papers with code
Out-of-Distribution Detection for Monocular Depth Estimation
In monocular depth estimation, uncertainty estimation approaches mainly target the data uncertainty introduced by image noise.
Heatmap-based Out-of-Distribution Detection
Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in-distribution samples and high response heatmaps for OOD samples, based on the classifier features and the class prediction.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Gradient-based Uncertainty for Monocular Depth Estimation
To avoid relying on ground-truth information for the loss definition, we present an auxiliary loss function based on the correspondence of the depth prediction for an image and its horizontally flipped counterpart.
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware
We select the task of monocular depth estimation where our goal is to transform a pre-trained model to the target's domain data.
Few-Shot Microscopy Image Cell Segmentation
Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem.
Estimating User Communication Styles for Spoken Dialogue Systems
We use this corpus to estimate the elaborateness and the directness of each utterance.
