GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
This addresses the limitation of fixed video representations in content search and exploration for users, though it appears incremental as it builds on existing multi-modal video summarization benchmarks.
The paper tackles the problem of generating video summaries based on user text queries by proposing a new method using a specialized attention network and contextualized word representations, resulting in a +5.88% increase in accuracy and +4.06% increase in F1-score compared to the state-of-the-art method.
Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding the text-based query and the video effectively are both important for the task of multi-modal video summarization. In this work, a new method is proposed that uses a specialized attention network and contextualized word representations to tackle this task. The proposed model consists of a contextualized video summary controller, multi-modal attention mechanisms, an interactive attention network, and a video summary generator. Based on the evaluation of the existing multi-modal video summarization benchmark, experimental results show that the proposed model is effective with the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared with the state-of-the-art method.