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Found 5 papers, 2 papers with code
Evidential Deep Learning for Class-Incremental Semantic Segmentation
In this paper, we address the problem of how to model unlabeled classes while avoiding spurious feature clustering of future uncorrelated classes.
DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models
Since such a simplification of the heatmaps removes valid information about possibly correct, though labeled unlikely, joint locations, we propose to represent the heatmaps as a set of 2D joint candidate samples.
Modeling Unknown Semantic Labels as Uncertainty in the Prediction: Evidential Deep Learning for Class-Incremental Semantic Segmentation
In this paper we address the problem of how to model unlabeled classes to avoid unnecessary feature clustering of uncorrelated classes.
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
We trained our system using both ground truth and estimated semantic segmentation input.
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction.
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