# Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

arXiv: https://arxiv.org/abs/1911.05075 · HTML: https://mlsanity.com/paper/1911.05075/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 20 of 20)

- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification](https://mlsanity.com/paper/2207.06104/) (2022)
- [Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects](https://mlsanity.com/paper/2210.02074/) (2022)
- [Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors](https://mlsanity.com/paper/2107.04517/) (2021)
- [Towards Unsupervised Open World Semantic Segmentation](https://mlsanity.com/paper/2201.01073/) (2022)
- [MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification](https://mlsanity.com/paper/2405.19186/) (2024)
- [Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation](https://mlsanity.com/paper/2303.06920/) (2023)
- [Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes](https://mlsanity.com/paper/2309.04302/) (2023)
- [Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation](https://mlsanity.com/paper/2310.17436/) (2023)
- [False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates](https://mlsanity.com/paper/2106.14474/) (2021)
- [Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation](https://mlsanity.com/paper/2305.12825/) (2023)
- [False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation](https://mlsanity.com/paper/2207.03513/) (2022)
- [Decoupling of neural network calibration measures](https://mlsanity.com/paper/2406.02411/) (2024)
- [False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2110.15681/) (2021)
- [Learning to Generate Training Datasets for Robust Semantic Segmentation](https://mlsanity.com/paper/2308.02535/) (2023)
- [Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis](https://mlsanity.com/paper/2408.10021/) (2024)
- [Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning](https://mlsanity.com/paper/2205.14917/) (2022)
- [Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks](https://mlsanity.com/paper/2409.11373/) (2024)
- [Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration](https://mlsanity.com/paper/2412.13695/) (2024)
- [Spatio-Temporal Attention for Consistent Video Semantic Segmentation in Automated Driving](https://mlsanity.com/paper/2602.10052/) (2026)

## Abstract (arXiv)

In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in applications like automated driving, video streams of images are available, we present a time-dynamic approach to investigating uncertainties and assessing the prediction quality of neural networks. We track segments over time and gather aggregated metrics per segment, thus obtaining time series of metrics from which we assess prediction quality. This is done by either classifying between intersection over union equal to 0 and greater than 0 or predicting the intersection over union directly. We study different models for these two tasks and analyze the influence of the time series length on the predictive power of our metrics.