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arXiv · 2303.15993

SELF-VS: Self-supervised Encoding Learning For Video Summarization

Hojjat Mokhtarabadi·Kave Bahraman·Mehrdad HosseinZadeh·Mahdi Eftekhari
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Abstract

Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are prone to overfitting. To mitigate this challenge, we propose a novel self-supervised video representation learning method using knowledge distillation to pre-train a transformer encoder. Our method matches its semantic video representation, which is constructed with respect to frame importance scores, to a representation derived from a CNN trained on video classification. Empirical evaluations on correlation-based metrics, such as Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our approach compared to existing state-of-the-art methods in assigning relative scores to the input frames.

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Hojjat Mokhtarabadi, Kave Bahraman, Mehrdad HosseinZadeh, Mahdi Eftekhari. 2023-03-28. SELF-VS: Self-supervised Encoding Learning For Video Summarization. https://arxiv.org/abs/2303.15993

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