Deep-learning-based approaches for semantic segmentation of natural scene images: A review
The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
Ambiguous medical image segmentation using diffusion models
A Rahman, JMJ Valanarasu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …
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 …
[HTML][HTML] A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
Graph-based lstm for anti-money laundering: Experimenting temporal graph convolutional network with bitcoin data
I Alarab, S Prakoonwit - Neural Processing Letters, 2023 - Springer
Elliptic data—one of the largest Bitcoin transaction graphs—has admitted promising results
in many studies using classical supervised learning and graph convolutional network …
in many studies using classical supervised learning and graph convolutional network …
Deep learning for visual localization and mapping: A survey
Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …
new research direction and receive significant attention from both industry and academia …
Advances in the application of deep learning methods to digital rock technology.
X Li, B Li, F Liu, T Li, X Nie - Advances in Geo-Energy …, 2023 - search.ebscohost.com
Digital rock technology is becoming essential in reservoir engineering and petrophysics.
Three-dimensional digital rock reconstruction, image resolution enhancement, image …
Three-dimensional digital rock reconstruction, image resolution enhancement, image …
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
Modeling the distributional uncertainty for salient object detection models
Most of the existing salient object detection (SOD) models focus on improving the overall
model performance, without explicitly explaining the discrepancy between the training and …
model performance, without explicitly explaining the discrepancy between the training and …
Semi-supervised domain generalization with stochastic stylematch
Ideally, visual learning algorithms should be generalizable, for dealing with any unseen
domain shift when deployed in a new target environment; and data-efficient, for reducing …
domain shift when deployed in a new target environment; and data-efficient, for reducing …