Visual Relationship Detection (VRD) is a relatively young research area, where the goal is to develop prediction models for detecting the relationships between objects depicted in an image. A relationship is modeled as ...

Neural networks have been notorious for being computational expensive. Their demand for hardware resources prohibits their extensive use in embedded devices and puts restrictions on tasks like real time tracking. On top ...

With the adoption of Deep Learning (DL) systems within the security and safetycritical domains, a variety of traditional testing techniques, novel techniques, and new ideas are increasingly being adopted and implemented ...

Current state-of-the-art translation systems for speech-to-speech rely heavily on a text representation for the translation. By transcoding speech to text we lose important information about the characteristics of the ...

Poverty eradication is an inexorable process in human growth [21], with poverty estimation being the first and most important stage. Identifying strategies for poverty reduction programs and distributing resources ...

With the continued growth of deep learning models in terms of size and computational requirements, the need for efficient models for deployment on resource-constrained devices becomes crucial. Structured pruning has emerged ...

This Master’s thesis explores the application of Large Language Models (LLMs) for automating the process of ensuring requirement compliance in automotive software integration testing. As the automotive industry increasingly ...

The development of complex deep learning models requires extensive labeled datasets, which are scarce in specialized fields due to the labor-intensive and time consuming nature of data labeling. This project aims to ...