Video highlight detection is a crucial task in the field of video content analysis, aiming to automatically identify and extract the most important or engaging segments from lengthy video content. This capability can significantly enhance user experiences by providing quick access to relevant content across various domains.
Current methods for video highlight detection often depend on expensive, manually labeled frame-level annotations or require large external datasets for weak supervision through category information.
To address this challenge, in a new paper Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence, a research team from University of Saskatchewan and Google Research introduces an innovative unsupervised method for automatic video highlight detection, eliminating the requirements for manual annotations while achieving superior performance compared to previous methods.
The proposed approach leverages both audio and visual cues to enhance video highlight detection. This research is the first to exploit both modalities in an unsupervised learning context for this task, without relying on any large external datasets. The team observed that videos with similar content or actions often exhibit recurring key moments in both audio and visual modalities. These recurrences might manifest as specific sounds or phrases in the audio, or as particular objects or scenes in the visuals. These recurring elements serve as strong indicators of significant moments in the video.
By leveraging the strengths of both auditory and visual cues, the researchers developed an unsupervised algorithm that identifies these recurrences and uses them to generate a supervisory signal in the form of audio-visual pseudo-highlights. This signal trains a highlight detection network.
To eliminate the need for manual annotation, the team first uses a clustering technique to identify pseudo-categories of videos in the dataset. They then segment each video into clips and compare these clips across videos within the same pseudo-category, based on their audio and visual features. This comparison generates audio pseudo-highlight scores and visual pseudo-highlight scores. The scores are then aggregated to produce audio-visual pseudo-highlight scores, and the top-scoring clips are selected to compile the audio-visual pseudo-highlights used to train the network.
The resulting network employs unimodal and bimodal attention mechanisms. The unimodal attention mechanism captures relationships between video clips within the same modality, while the bimodal attention mechanism focuses on the interrelationship between the audio and visual modalities.
Empirical results demonstrate that the proposed method not only outperforms state-of-the-art unsupervised methods but also shows comparable or superior performance to state-of-the-art weakly supervised methods. This advancement contributes to the growing demand for automated and efficient video highlight detection techniques, paving the way for future research in unsupervised learning using audio and visual modalities for complex video understanding tasks.
The paper Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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