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

Autor:	Mokhtarabadi, Hojjat, Bahraman, Kave, HosseinZadeh, Mehrdad, Eftekhari, Mahdi
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.15993 Zobrazit plný text záznamu View this record from Arxiv