@inproceedings{d3d8f05854fc41f3838f644d89ecb959,
title = "Automated Contextual Tagging in Points-of-Interest Using Bag-of-Objects",
abstract = "Object detection is a fundamental task in computer vision, with its applications ranging from autonomous driving to scene recognition. In the domain of Points-of-Interest (POI), object recognition can aid in the analysis of complex urban environments that contain various types of infrastructure, people, and activities. This paper addresses this challenge by proposing a Bag-of-Objects (BoO) methodology for POI scene contextual tagging. The suggested approach utilizes a transformer-based model for object detection, followed by a threshold to assign tags to POI scenes. These tags are then used to train a novel multimodal model, called WORLD (Weight Optimization for Representation and Labeling Descriptions), which is capable of classifying and contextually tagging POI scenes based on their visual features.",
keywords = "Artificial Intelligence, Bag-of-Objects, Data Science, Deep Learning, Points-of-Interest, Transformers",
author = "Paraskevas Messios and Ioanna Dionysiou and Harald Gjermundr{\o}d",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 4th International Conference on Innovations in Computing Research, ICR 2025 ; Conference date: 25-08-2025 Through 27-08-2025",
year = "2025",
doi = "10.1007/978-3-031-95652-2\_6",
language = "English",
isbn = "9783031956515",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "62--73",
editor = "Kevin Daimi and Abeer Alsadoon",
booktitle = "Proceedings of the 4th International Conference on Innovations in Computing Research, ICR 2025",
address = "Germany",
}