Cross-Modal learning for Audio-Visual Video Parsing

TL;DR

This paper introduces a novel cross-modal learning approach for audio-visual video parsing that detects event boundaries in videos for both audio and visual streams, improving upon existing methods.

Contribution

It proposes a new AVVP method utilizing adversarial training, global attention, and self-supervised pretraining to enhance cross-modal event detection.

Findings

Outperforms state-of-the-art on LLP dataset across five metrics.

Demonstrates effectiveness of pretraining, global attention, and adversarial training.

Provides extensive ablation studies validating key components.

Abstract

In this paper, we present a novel approach to the audio-visual video parsing (AVVP) task that demarcates events from a video separately for audio and visual modalities. The proposed parsing approach simultaneously detects the temporal boundaries in terms of start and end times of such events. We show how AVVP can benefit from the following techniques geared towards effective cross-modal learning: (i) adversarial training and skip connections (ii) global context aware attention and, (iii) self-supervised pretraining using an audio-video grounding objective to obtain cross-modal audio-video representations. We present extensive experimental evaluations on the Look, Listen, and Parse (LLP) dataset and show that we outperform the state-of-the-art Hybrid Attention Network (HAN) on all five metrics proposed for AVVP. We also present several ablations to validate the effect of pretraining, global attention and adversarial training.