MF2Summ: Multimodal Fusion for Video Summarization with Temporal Alignment

• URL: http://arxiv.org/abs/2506.10430v1
• Date: Thu, 12 Jun 2025 07:32:51 GMT
• Title: MF2Summ: Multimodal Fusion for Video Summarization with Temporal Alignment
• Authors: Shuo wang, Jihao Zhang,
• Abstract summary: This paper introduces MF2Summ, a novel video summarization model based on multimodal content understanding.<n>MF2Summ employs a five-stage process: feature extraction, cross-modal attention interaction, feature fusion, segment prediction, and key shot selection.<n> Experimental results on the SumMe and TVSum datasets demonstrate that MF2Summ achieves competitive performance.
• Score: 5.922172844641853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid proliferation of online video content necessitates effective video summarization techniques. Traditional methods, often relying on a single modality (typically visual), struggle to capture the full semantic richness of videos. This paper introduces MF2Summ, a novel video summarization model based on multimodal content understanding, integrating both visual and auditory information. MF2Summ employs a five-stage process: feature extraction, cross-modal attention interaction, feature fusion, segment prediction, and key shot selection. Visual features are extracted using a pre-trained GoogLeNet model, while auditory features are derived using SoundNet. The core of our fusion mechanism involves a cross-modal Transformer and an alignment-guided self-attention Transformer, designed to effectively model inter-modal dependencies and temporal correspondences. Segment importance, location, and center-ness are predicted, followed by key shot selection using Non-Maximum Suppression (NMS) and the Kernel Temporal Segmentation (KTS) algorithm. Experimental results on the SumMe and TVSum datasets demonstrate that MF2Summ achieves competitive performance, notably improving F1-scores by 1.9\% and 0.6\% respectively over the DSNet model, and performing favorably against other state-of-the-art methods.

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