Stage-Oriented Analysis of Video Summarization Models Across Learning Paradigms and Benchmark Datasets

Resumen

Video summarization is to create concise summaries for longer videos. Although many researchers contribute with great advancements in summarization process, there are also significant obstacles to overcome. One prime challenge is the subjective nature of determining the important elements in the video based on the viewer. Another major issues are due to the various stages in a traditional pipeline, where an error at one level can result in failure of the entire summarization process. In addition, many current methods unable to retain connections between elements throughout the videos, and scalability continues to be an obstacle for the creation of lengthy summaries. This review categorizes video summarization process into stages, which include video preparation and segmentation, extracting features from different sources like visual, audio or text features, modeling patterns based on time, scoring for relevance, and finalizing the summary. Traditional approaches relied on basic machine learning models, where the modern approaches use advanced deep learning and attention-based methods that can better capture global context and semantics of a video. Challenges still exist, such as high computational costs, heavy reliance on human annotation and having a bias in the use of data sets. This survey compiles evidence from recent benchmark studies to better understand transformer based self-supervised and reinforcement learning techniques can address the limitations of traditional approaches but also introduce new concerns related to computation complexity and interpretability. Furthermore, this review provides future research directions to achieve an integrated, end-to-end, and user-preferred system and conclude that multimodal reasoning as a key enabler for ensuring high-quality, scalable, and semantically consistent systems for video summarization applications.