Deep Deterministic Uncertainty for Semantic Segmentation

2021-10-29 · Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. DDU enables quantifying and disentangling epistemic and aleatoric uncertainty in a single forward pass through the model. We study the similarity of feature representations of pixels at different locations for the same class and conclude that it is feasible to apply DDU location independently, which leads to a significant reduction in memory consumption compared to pixel dependent DDU. Using the DeepLab-v3+ architecture on Pascal VOC 2012, we show that DDU improves upon MC Dropout and Deep Ensembles while being significantly faster to compute.

📄 PDF Abstract BibTeX arXiv:2111.00079

Code (0)

등록된 구현이 없습니다.

Tasks

Segmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Deep Ensembles 설명 없음

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation

2026-05-20 · Kerim Turacan, Hannes Reichert, Andrei Bolandut, Konrad Doll arxiv

LiDAR semantic segmentation is a core perception capability for autonomous vehicles and mobile robots. However, safe operation also depends on knowing when predictions are unreliable. Existing approaches typically rely o…

LIDAR Semantic Segmentation Autonomous Vehicles

Latent Discriminant deterministic Uncertainty

2022-07-20 · Gianni Franchi, Xuanlong Yu, Andrei Bursuc, Emanuel Aldea 외

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address t…

Autonomous Driving Depth Estimation image-classification Image Classification+3

Deep Deterministic Uncertainty: A New Simple Baseline

2023-01-01 · CVPR 2023 1 · Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H.S. Torr 외

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass unc…

Active Learning Semantic Segmentation Uncertainty Quantification

U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration

2026-05-14 · Michael Smith, Frank P. Ferrie arxiv

In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that the quality of uncertainty estimates can be very sensitive to a rang…

Panoptic Segmentation

Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation

2026-03-20 · Yifei Zhao, Fanyu Zhao, Yinsheng Li arxiv

Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic…

Point Cloud Segmentation 3D Semantic Segmentation Point Clouds