TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization

Sumin Kim, Hyemin Jeong, Mingu Kang, Yejin Kim, Yoori Oh, Joonseok Lee

Keywords: Video Summarization, Video Understanding, Multimodal Learning

TL;DR: We propose TripleSumm, a frame-level adaptive multimodal fusion model, and introduce MoSu, the first large-scale benchmark with all three modalities, achieving state-of-the-art video summarization performance.

Abstract: The exponential growth of video content necessitates effective video summarization to efficiently extract key information from long videos. However, current approaches struggle to fully comprehend complex videos, primarily because they employ static or modality-agnostic fusion strategies. These methods fail to account for the dynamic, frame-dependent variations in modality saliency inherent in video data. To overcome these limitations, we propose **TripleSumm**, a novel architecture that adaptively weights and fuses the contributions of visual, text, and audio modalities at the frame level. Furthermore, a significant bottleneck for research into multimodal video summarization has been the lack of comprehensive benchmarks. Addressing this bottleneck, we introduce **MoSu** (Most Replayed Multimodal Video Summarization), the first large-scale benchmark that provides all three modalities. Extensive experiments demonstrate that TripleSumm achieves state-of-the-art performance, outperforming existing methods by a significant margin on four benchmarks, including MoSu. Our code and dataset are available at https://github.com/smkim37/TripleSumm.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 2952