Review on deep learning approaches for anomaly event detection in video surveillance
In the last few years, due to the continuous advancement of technology, human behavior
detection and recognition have become important scientific research in the field of computer …
detection and recognition have become important scientific research in the field of computer …
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Recent intelligent fault diagnosis technologies can effectively identify the machinery health
condition, while they are learnt based on a closed-world assumption, ie, the training and …
condition, while they are learnt based on a closed-world assumption, ie, the training and …
Anomaly detection in autonomous driving: A survey
D Bogdoll, M Nitsche… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …
roads. While the perception of autonomous vehicles performs well under closed-set …
Introspection of dnn-based perception functions in automated driving systems: State-of-the-art and open research challenges
Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
Siren: Shaping representations for detecting out-of-distribution objects
Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object
detectors in the wild. Although distance-based OOD detection methods have demonstrated …
detectors in the wild. Although distance-based OOD detection methods have demonstrated …
Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data
Deep semi-supervised learning (SSL) methods aim to take advantage of abundant
unlabeled data to improve the algorithm performance. In this paper, we consider the …
unlabeled data to improve the algorithm performance. In this paper, we consider the …
Run-time monitoring of machine learning for robotic perception: A survey of emerging trends
As deep learning continues to dominate all state-of-the-art computer vision tasks, it is
increasingly becoming an essential building block for robotic perception. This raises …
increasingly becoming an essential building block for robotic perception. This raises …
Projection regret: Reducing background bias for novelty detection via diffusion models
Novelty detection is a fundamental task of machine learning which aims to detect abnormal
(ie out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the …
(ie out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the …
Out-of-distribution (OOD) detection based on deep learning: A review
P Cui, J Wang - Electronics, 2022 - mdpi.com
Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from
input data through a model. This problem has attracted increasing attention in the area of …
input data through a model. This problem has attracted increasing attention in the area of …
Interpretable self-aware neural networks for robust trajectory prediction
M Itkina, M Kochenderfer - Conference on Robot Learning, 2023 - proceedings.mlr.press
Although neural networks have seen tremendous success as predictive models in a variety
of domains, they can be overly confident in their predictions on out-of-distribution (OOD) …
of domains, they can be overly confident in their predictions on out-of-distribution (OOD) …