@inproceedings{6bdc5d9126b449bbaabc2eeb13f7b99d,
title = "VCR: Video representation for Contextual Retrieval",
abstract = "Streamlining content discovery in media archives requires advanced data representations and effective visualization techniques for clear communication of video topics to users. The proposed system addresses the challenge of efficiently navigating large video collections by exploiting a fusion of visual, audio, and textual features to accurately index and categorize video content through a text-based method. Additionally, semantic embeddings are employed to provide contextually relevant information and recommendations to users, resulting in an intuitive and engaging exploratory experience over our topics ontology map using LLMs (GitHub).",
keywords = "Archive Exploration, Media Search, Video Representation",
author = "Oron Nir and Idan Vidra and Avi Neeman and Barak Kinarti and Ariel Shamir",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024 ; Conference date: 12-04-2024 Through 14-04-2024",
year = "2024",
month = apr,
day = "12",
doi = "10.1145/3661725.3661766",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "CMLDS 2024 - 2024 International Conference on Computing, Machine Learning and Data Science, Conference Proceedings",
}