Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video

Understand

Current video summarization methods rely heavily on supervised computer vision techniques, which demands time-consuming and subjective manual annotations.

• To overcome these limitations, we investigated self-supervised video summarization.
• Inspired by the success of Large Language Models (LLMs), we explored the feasibility in transforming the video summarization task into a Natural Language Processing (NLP) task.
• By leveraging the advantages of LLMs in context understanding, we aim to enhance the effectiveness of self-supervised video summarization.