Deep Learning Advancements And Challeng In

Abstract

With the exponential rise of video content on social media platforms, particularly YouTube, which handles over 500 hours of uploads every minute, efficient video indexing, retrieval, and summarization have become critical challenges. Traditional methods rely heavily on user-provided metadata such astitles, tags, and descriptions, which are often inaccurate or unrelated to the actual content. To overcome these limitations, recent advances in vision-language models, such as BLIP (Bootstrapping Language-Image Pretraining) transformers, have enabled more accurate and automated video understanding by jointly learning from visual and textual modalities.

This paper presents a systematic review of deep learning-based video summarization approaches, with a particular emphasis on BLIP-based models and their potential to bridge the gap between raw video content and semantic interpretation. Out of more than 300 research studies, 44 were shortlisted using strict inclusion criteria, and their methodologies, applications, and datasets are critically analyzed. The review highlights how BLIP transformers enhance summarization performance by generating context-aware captions, enabling semantic indexing, and improving retrieval efficiency. The insights provided in this study offer valuable guidance for researchers and practitioners aiming to leverage deep learning and vision-language models for managing largescale video data in social networking platforms.