Video Summarization

Student: Kolmakova Tatiana

Educational Programme: Data Science (Master)

Year of Graduation: 2021

The amount of user-generated content is increasing on a daily basis. This is especially true for video content that became popular with social media like TikTok. Other internet sources keep up and easier the way for video sharing. That is why automatic tools for finding core information of content but decreasing its volume are essential. Video summarization is aimed to help with it. In this work, we propose a transformer-based approach to supervised video sum- marization. Previous works that used attention architectures either used lighter versions or loaded models with RNN modules, that slower computations. Our pro- posed framework uses all advantages of transformers. Extensive evaluation on two benchmark datasets showed that introduced model outperform existed approaches on SumMe dataset by 3% and shows comparable results on TVSum dataset.

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