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Deep Deterministic Uncertainty for Semantic Segmentation
[article]
2021
arXiv
pre-print
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We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. ...
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. ...
Conclusion In this paper, we show that Deep Deterministic Uncertainty (DDU) can be easily extended to the task of semantic segmentation. ...
arXiv:2111.00079v1
fatcat:xal74vuiwfdongiio2d3dy2p3e
On the Practicality of Deterministic Epistemic Uncertainty
[article]
2021
arXiv
pre-print
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Then, we extend the most promising approaches to semantic segmentation. ...
A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks. ...
In the dilated resnet architecture used for semantic segmentation the latent representation z is passed through a point-wise feedforward layer f : R Wz×Hz×Cz → R Wz×Hz×3 and, subsequently, bilinearly upsampled ...
arXiv:2107.00649v2
fatcat:hpo6z42vifej7lgdmx4tje6scu
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
[article]
2019
arXiv
pre-print
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Annotating the right data for training deep neural networks is an important challenge. ...
We conduct a series of large-scale visual active learning experiments to evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet datasets, and semantic segmentation with the BDD100k dataset ...
Active Semantic Segmentation The goal of semantic segmentation is to assign a class label to every pixel in an image. ...
arXiv:1811.03575v3
fatcat:5luahiq67zbv7ffvyqbauvm4uu
Bayesian deep learning of affordances from RGB images
[article]
2021
arXiv
pre-print
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However, previous works assume a deterministic model, but uncertainty quantification is fundamental for robust detection, affordance-based reason, continual learning, etc. ...
For comparison, we estimate the uncertainty using two state-of-the-art techniques: Monte Carlo dropout and deep ensembles. We also compare different types of CNN encoders for feature extraction. ...
Kendall and Gal [10] analyzed both uncertainties in common computer vision tasks (semantic segmentation and depth regression) to show that the aleatoric and epistemic uncertainty model different phenomenons ...
arXiv:2109.12845v1
fatcat:n7gqlxomina75msjhret3gak7i
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
[article]
2021
arXiv
pre-print
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We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our ...
We define STL-U strong and weak satisfaction semantics based on if all or some sequences contained in a flowpipe satisfy the requirement. ...
Uncertainty Estimation with Bayesian RNN Models Stochastic regularization techniques (SRTs) have been popularly used to cast deterministic deep learning models as Bayesian models for uncertainty estimation ...
arXiv:2011.00384v3
fatcat:gxufwlzxnbgqvja7wwtkqtey5i
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
[article]
2022
arXiv
pre-print
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The developed approach enables reliable, safe landing site detection by: (i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation ...
In response to these limitations, this paper proposes an application of the Bayesian deep-learning segmentation method for hazard detection. ...
Bayesian Deep Learning for Semantic Segmentation Figure 2 shows the network architecture used for the semantic segmentation stage, which is based on Bayesian SegNet [34] . ...
arXiv:2102.10545v2
fatcat:fbgg4zwn5jduvlxwsdlzbxyroa
Scalable Uncertainty for Computer Vision with Functional Variational Inference
[article]
2020
arXiv
pre-print
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As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world ...
segmentation. ...
We would like to thank Jan Czarnowski, Sajad Saeedi, Tristan Laidlow and all our reviewers for helpful insights and comments. ...
arXiv:2003.03396v1
fatcat:3nwrztbfhnf37ln27j6xrvgute
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1811.12709v1 fatcat:2wjugpomczgqxjzpzlamw2pboy
Deep learning has been revolutionary for computer vision and semantic segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain uncertainty maps from deep models when predicting semantic ...
This information is critical when using semantic segmentation for autonomous driving for example. Standard semantic segmentation systems have well-established evaluation metrics. ...
There has been a lot of research on deep architectures for semantic segmentation [46, 55, 5, 9, 11, 10, 13, 66] . ...
arXiv:1811.12709v2
fatcat:lqtc6w3n6jehtcma4m3athavwu
Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception
[article]
2019
arXiv
pre-print
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Experiments are conducted using a deep learning semantic image segmentation, followed by a path planner based on the resulting cost map, to provide an empirical analysis of the proposed method. ...
This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scenarios. ...
In another work, the uncertainty from dropout is used in semantic segmentation for improved learning and test time estimation [12] . ...
arXiv:1910.00101v1
fatcat:wzhsojsfbrh7vh54gbug3imfui
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
[article]
2021
arXiv
pre-print
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Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. ...
Instead, this work aims to achieve semantic segmentation by scribble annotations directly without extra information and other limitations. ...
To our best knowledge, minimizing entropy is adopted to scribble-supervised semantic segmentation for uncertainty reduction for the first time. ...
arXiv:2102.09896v1
fatcat:adxb7ie5jjgmvcvcc7yl6pw6om
Uncertainty Aware Proposal Segmentation for Unknown Object Detection
[article]
2021
arXiv
pre-print
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semantic segmentation. ...
We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation using Radial Basis Functions Networks (RBFN) for class agnostic ...
Introduction The last decade marked big progress in the design of Deep Network models for object detection and semantic segmentation. ...
arXiv:2111.12866v1
fatcat:bx4dolp2hvgmvbywyubj7dk65a
A Review on Deep Learning Techniques for Video Prediction
[article]
2020
arXiv
pre-print
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Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. ...
In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. ...
Despite its popularity, the original dataset did not contain ground truth for semantic segmentation. ...
arXiv:2004.05214v2
fatcat:weerbkanmjb4dn6wkn5o4b5aia
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
[article]
2020
arXiv
pre-print
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Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation. Such segmentation may still be noisy. ...
Our contributions are two-fold: (i) we show how to use Bayesian deep learning techniques to extract risk at the perception level; and (ii) use a risk-theoretical measure, CVaR, for risk-aware planning ...
We implement a deep learning technique for semantic segmentation of the overhead image. Due to the uncertainty from segmentation, the travel cost of the vehicle turns out to be a random variable. ...
arXiv:2003.11675v2
fatcat:w6rb76tm7ze33ds6alsbgn2tui
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
[article]
2016
arXiv
pre-print
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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. ...
Real Time Performance
Conclusions We have presented Bayesian SegNet, the first probabilistic framework for semantic segmentation using deep learning, which outputs a measure of model uncertainty for ...
arXiv:1511.02680v2
fatcat:2rc2pxlzm5ddlkksy7cmyhfoam
Vector Quantized Bayesian Neural Network Inference for Data Streams
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1907.05911v2 fatcat:rbzdwkpagzgunnrcsvjzt5uymyOther Versions
Experiments including semantic segmentation on real-world data show that this model performs significantly faster than BNNs while estimating predictive results comparable to or superior to the results ...
Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). ...
Semantic Segmentation Semantic segmentation experiment, which is a pixel-wise classification, evaluates the computational and predictive performance of VQ-BNN with a modern deep NN in practical situation ...
arXiv:1907.05911v3
fatcat:zn6zjnpbubdfpi2rw3zsbe22oi
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