Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
The recent series of innovations in deep learning (DL) have shown enormous potential to
impact individuals and society, both positively and negatively. DL models utilizing massive …
impact individuals and society, both positively and negatively. DL models utilizing massive …
Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach
Social media is used to categorise products or services, but analysing vast comments is time-
consuming. Researchers use sentiment analysis via natural language processing …
consuming. Researchers use sentiment analysis via natural language processing …
Explainable ai for safe and trustworthy autonomous driving: A systematic review
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks
in autonomous driving (AD) due to its superior performance compared to conventional …
in autonomous driving (AD) due to its superior performance compared to conventional …
Image translation as diffusion visual programmers
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
[HTML][HTML] Knowledge-infused learning for entity prediction in driving scenes
Scene understanding is a key technical challenge within the autonomous driving domain. It
requires a deep semantic understanding of the entities and relations found within complex …
requires a deep semantic understanding of the entities and relations found within complex …
Traffic-domain video question answering with automatic captioning
Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced
machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and …
machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and …
Knowledge augmented machine learning with applications in autonomous driving: A survey
The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …
artificial intelligence and machine learning models. However, in real life applications these …
Towards semantic integration of machine vision systems to aid manufacturing event understanding
A manufacturing paradigm shift from conventional control pyramids to decentralized, service-
oriented, and cyber-physical systems (CPSs) is taking place in today's 4th industrial …
oriented, and cyber-physical systems (CPSs) is taking place in today's 4th industrial …
Knowledge-infused deep learning
Deep Learning has shown remarkable success during the last decade for essential tasks in
computer vision and natural language processing. Yet, challenges remain in the …
computer vision and natural language processing. Yet, challenges remain in the …
Intelligent traffic monitoring with hybrid ai
E Qasemi, A Oltramari - arXiv preprint arXiv:2209.00448, 2022 - arxiv.org
Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and
modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We …
modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We …