Exploring the Role of Audio in Video Captioning

TL;DR

This paper introduces an audio-visual framework for video captioning that leverages raw audio signals, proposes a modality balanced pre-training loss, and enhances cross-modal fusion, leading to improved performance without relying on text transcripts.

Contribution

It presents a novel modality balanced pre-training loss and new fusion mechanisms to better utilize audio in video captioning, outperforming existing methods on multiple datasets.

Findings

Significant performance improvements on four datasets.

Outperforms state-of-the-art methods on some metrics.

Effective use of raw audio signals enhances captioning quality.

Abstract

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.