From Theory To Practice: Unsupervised, Weakly Supervised, And Reinforcement Learning Approaches To Video Summarization

The ease of access to the internet and technological advancements have led to an enormous amount of audio-visual content generated daily. The processing time and amount of space required for this multimedia content reduces the functionality of many applications that rely on video processing, including indexing, searching, and recommendation. The field of video summarizing focuses on methods for making videos shorter. This literature review provides a comprehensive analysis of the current advancements in video summarization, focusing on three primary methodologies: unsupervised learning-based techniques, weakly supervised learning-based techniques, and reinforcement learning-based techniques. The review explores the underlying principles, methodologies, and the effectiveness of various approaches within these categories. Unsupervised learning techniques are examined for their ability to generate video summaries without the need for labeled data, offering flexible solutions for diverse video types. Weakly supervised learning methods are evaluated for their capacity to leverage minimal labeled data while maintaining robust performance in generating summaries. Reinforcement learning-based techniques are analyzed for their sequential decision-making processes, which optimize the selection of key frames or segments in a video. By critically assessing these approaches, the review aims to highlight their strengths, limitations, and potential areas for future research in the field of video summarization.