pith. sign in

arxiv: 1511.02680 · v2 · pith:6YMTT3XCnew · submitted 2015-11-09 · 💻 cs.CV · cs.NE

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

Alex Kendall , Vijay Badrinarayanan , Roberto Cipolla This is my paper
classification 💻 cs.CV cs.NE
keywords uncertaintysegnetbayesianscenesegmentationunderstandingarchitecturesclass
0
0 comments X
read the original abstract

We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making. Our contribution is a practical system which is able to predict pixel-wise class labels with a measure of model uncertainty. We achieve this by Monte Carlo sampling with dropout at test time to generate a posterior distribution of pixel class labels. In addition, we show that modelling uncertainty improves segmentation performance by 2-3% across a number of state of the art architectures such as SegNet, FCN and Dilation Network, with no additional parametrisation. We also observe a significant improvement in performance for smaller datasets where modelling uncertainty is more effective. We benchmark Bayesian SegNet on the indoor SUN Scene Understanding and outdoor CamVid driving scenes datasets.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation

    cs.AR 2026-04 unverdicted novelty 7.0

    Proposes dropout-based BayesCVNNs with automated configuration search and FPGA accelerators that deliver 4.5x–13x speedups over GPUs while enabling uncertainty estimation for complex-valued neural networks.

  2. UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation

    cs.CV 2026-05 unverdicted novelty 6.0

    UnGAP turns aleatoric uncertainty into an active calibration signal via affine feature modulation to fix gradient suppression in heteroscedastic crack segmentation while maintaining real-time performance.

  3. Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation

    cs.CV 2025-02 unverdicted novelty 5.0

    Uncertainty-weighted ensemble learning achieves near state-of-the-art overall segmentation accuracy while outperforming prior models on end slices in cardiac MRI.

  4. Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving

    cs.CV 2025-01 unverdicted novelty 5.0

    Vision foundation model embeddings with density modeling outperform state-of-the-art methods for unsupervised semantic and covariate shift detection in autonomous driving inputs.