# Detection of False Positive and False Negative Samples in Semantic Segmentation

arXiv: https://arxiv.org/abs/1912.03673 · HTML: https://mlsanity.com/paper/1912.03673/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations](https://mlsanity.com/paper/2210.08345/) (2022)
- [Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors](https://mlsanity.com/paper/2107.04517/) (2021)
- [Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels](https://mlsanity.com/paper/2108.04114/) (2021)
- [Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization](https://mlsanity.com/paper/2211.05233/) (2022)
- [LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds](https://mlsanity.com/paper/2306.07835/) (2023)
- [A New Perspective On AI Safety Through Control Theory Methodologies](https://mlsanity.com/paper/2506.23703/) (2025)
- [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)
- [Spatio-Temporal Attention for Consistent Video Semantic Segmentation in Automated Driving](https://mlsanity.com/paper/2602.10052/) (2026)

## Abstract (arXiv)

In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions.