Query-based Video Summarization with Pseudo Label Supervision

• URL: http://arxiv.org/abs/2307.01945v1
• Date: Tue, 4 Jul 2023 22:28:17 GMT
• Title: Query-based Video Summarization with Pseudo Label Supervision
• Authors: Jia-Hong Huang, Luka Murn, Marta Mrak, Marcel Worring
• Abstract summary: Existing datasets for manually labelled query-based video summarization are costly and thus small. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels. Experimental results show that the proposed video summarization algorithm achieves state-of-the-art performance.
• Score: 19.229722872058055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels to pre-train a supervised deep model. In this work, we introduce segment-level pseudo labels from input videos to properly model both the relationship between a pretext task and a target task, and the implicit relationship between the pseudo label and the human-defined label. The pseudo labels are generated based on existing human-defined frame-level labels. To create more accurate query-dependent video summaries, a semantics booster is proposed to generate context-aware query representations. Furthermore, we propose mutual attention to help capture the interactive information between visual and textual modalities. Three commonly-used video summarization benchmarks are used to thoroughly validate the proposed approach. Experimental results show that the proposed video summarization algorithm achieves state-of-the-art performance.

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