AudioVisual Video Summarization

TL;DR

This paper introduces a novel audiovisual recurrent network that jointly leverages audio and visual data to improve video summarization, demonstrating superior performance over visual-only methods on benchmark datasets.

Contribution

It proposes the AVRN model that fuses audio and visual features using a three-part architecture, enhancing video understanding for summarization.

Findings

AVRN outperforms visual-only methods on SumMe and TVsum datasets.

Each component of AVRN contributes significantly to overall performance.

Multimodal fusion improves the quality of video summaries.

Abstract

Audio and vision are two main modalities in video data. Multimodal learning, especially for audiovisual learning, has drawn considerable attention recently, which can boost the performance of various computer vision tasks. However, in video summarization, existing approaches just exploit the visual information while neglect the audio information. In this paper, we argue that the audio modality can assist vision modality to better understand the video content and structure, and further benefit the summarization process. Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this. Specifically, the proposed AVRN can be separated into three parts: 1) the two-stream LSTM is utilized to encode the audio and visual feature sequentially by capturing their temporal dependency. 2) the audiovisual fusion LSTM is employed to fuse the two modalities by exploring the latent consistency between them. 3) the self-attention video encoder is adopted to capture the global dependency in the video. Finally, the fused audiovisual information, and the integrated temporal and global dependencies are jointly used to predict the video summary. Practically, the experimental results on the two benchmarks, \emph{i.e.,} SumMe and TVsum, have demonstrated the effectiveness of each part, and the superiority of AVRN compared to those approaches just exploiting visual information for video summarization.