Manolis Mylonas

Manolis Mylonas, Charalampia Zerva, Evlampios Apostolidis et al.

In this work, we extend a recent method for script-driven video summarization, originally considering just the visual content of the video, to take into account the relevance of the user-provided script also with the video's spoken content. In the proposed method, SD-MVSum, the dependence between each considered pair of data modalities, i.e., script-video and script-transcript, is modeled using a new weighted cross-modal attention mechanism. This explicitly exploits the semantic similarity between the paired modalities in order to promote the parts of the full-length video with the highest relevance to the user-provided script. Furthermore, we extend two large-scale datasets for video summarization (S-VideoXum, MrHiSum), to make them suitable for training and evaluation of script-driven multimodal video summarization methods. Experimental comparisons document the competitiveness of our SD-MVSum method against other SOTA approaches for script-driven and generic video summarization. Our new method and extended datasets are available at: https://github.com/IDT-ITI/SD-MVSum.

Manolis Mylonas, Evlampios Apostolidis, Vasileios Mezaris

In this work, we introduce the task of script-driven video summarization, which aims to produce a summary of the full-length video by selecting the parts that are most relevant to a user-provided script outlining the visual content of the desired summary. Following, we extend a recently-introduced large-scale dataset for generic video summarization (VideoXum) by producing natural language descriptions of the different human-annotated summaries that are available per video. In this way we make it compatible with the introduced task, since the available triplets of ``video, summary and summary description'' can be used for training a method that is able to produce different summaries for a given video, driven by the provided script about the content of each summary. Finally, we develop a new network architecture for script-driven video summarization (SD-VSum), that employs a cross-modal attention mechanism for aligning and fusing information from the visual and text modalities. Our experimental evaluations demonstrate the advanced performance of SD-VSum against SOTA approaches for query-driven and generic (unimodal and multimodal) summarization from the literature, and document its capacity to produce video summaries that are adapted to each user's needs about their content.