Cross-Modal learning for Audio-Visual Video Parsing
This work addresses the problem of parsing events in videos for multimodal analysis, representing an incremental improvement over existing methods.
The paper tackles the audio-visual video parsing (AVVP) task by detecting temporal event boundaries separately for audio and visual modalities, achieving state-of-the-art performance on the LLP dataset by outperforming the Hybrid Attention Network across all five metrics.
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.