Multimodal Frame-Scoring Transformer for Video Summarization
This work addresses the problem of efficient video content browsing for users by improving automatic summarization, though it is incremental as it builds on existing multimodal approaches.
The paper tackles the problem of generic video summarization by addressing limitations in using only visual features and insufficient datasets, proposing the Multimodal Frame-Scoring Transformer (MFST) that exploits visual, text, and audio features, resulting in superior performance on TVSum and SumMe datasets with large margins in F1 score and Rank-based evaluation.
As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors. To address these two problems, this paper proposes the Multimodal Frame-Scoring Transformer (MFST), a framework exploiting visual, text, and audio features and scoring a video with respect to frames. Our MFST framework first extracts each modality features (audio-visual-text) using pretrained encoders. Then, MFST trains the multimodal frame-scoring transformer that uses multimodal representation based on extracted features as inputs and predicts frame-level scores. Our extensive experiments with previous models and ablation studies on TVSum and SumMe datasets demonstrate the effectiveness and superiority of our proposed method by a large margin in both F1 score and Rank-based evaluation.