3 citations · 6 across the 13 of their papers we have counts for
13 papers
Iterative Hybrid Discrete-Continuous Viewpoint Planning for UAV Photogrammetry
Alan Grech, Daniel Pisani, Andre Grima +3
Unmanned aerial vehicle (UAV) photogrammetry requires camera networks that provide sufficient surface coverage, image overlap, parallax, and resolution, yet conventional flight pat…
MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments
Andrea Filiberto Lucas, Mark Bugeja, Carl James Debono +1
Automated visual monitoring of urban environments is a growing Computer Vision research area, but municipal solid waste detection remains under-represented in dedicated benchmark r…
Large-Scale Photogrammetric Documentation of St. John's Co-Cathedral: A Workflow for Cultural Heritage Preservation
Matthew Kenely, Mark Bugeja, Andre Grima +3
We present a comprehensive methodology for the large-scale photogrammetric documentation of St. John's Co-Cathedral in Valletta, Malta, a UNESCO World Heritage site renowned for it…
A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video
Andrea Filiberto Lucas, Dylan Seychell
The growing volume of video-based news content has heightened the need for transparent and reliable methods to extract on-screen information. Yet the variability of graphical layou…
Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
Matthias Bartolo, Dylan Seychell, Gabriel Hili +4
This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available…
Learning Using Privileged Information for Litter Detection
Matthias Bartolo, Konstantinos Makantasis, Dylan Seychell
As litter pollution continues to rise globally, developing automated tools capable of detecting litter effectively remains a significant challenge. This study presents a novel appr…
Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation
Jonathan Attard, Dylan Seychell
News videos require efficient content organisation and retrieval systems, but their unstructured nature poses significant challenges for automated processing. This paper presents a…
A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces
Matthew Kenely, Dylan Seychell, Carl James Debono +1
News outlets' competition for attention in news interfaces has highlighted the need for demographically-aware saliency prediction models. Despite recent advancements in saliency de…
Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI
Ljubisa Bojic, Dylan Seychell, Milan Cabarkapa
As general-purpose artificial intelligence systems become increasingly integrated into society and are used for information seeking, content generation, problem solving, textual an…
Correlation of Object Detection Performance with Visual Saliency and Depth Estimation
Matthias Bartolo, Dylan Seychell
As object detection techniques continue to evolve, understanding their relationships with complementary visual tasks becomes crucial for optimising model architectures and computat…
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object Detection
Matthias Bartolo, Dylan Seychell, Josef Bajada
With the ever-growing variety of object detection approaches, this study explores a series of experiments that combine reinforcement learning (RL)-based visual attention methods wi…
AI as a Tool for Fair Journalism: Case Studies from Malta
Dylan Seychell, Gabriel Hili, Jonathan Attard +1
In today`s media landscape, the role of Artificial Intelligence (AI) in shaping societal perspectives and journalistic integrity is becoming increasingly apparent. This paper prese…
Predicting Relative Depth between Objects from Semantic Features
Stefan Cassar, Adrian Muscat, Dylan Seychell
Vision and language tasks such as Visual Relation Detection and Visual Question Answering benefit from semantic features that afford proper grounding of language. The 3D depth of o…