Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence

Zahidul Islam, Sujoy Paul, Mrigank Rochan; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8691-8700

With the exponential growth of video content the need for automated video highlight detection to extract key moments or highlights from lengthy videos has become increasingly pressing. This technology has the potential to enhance user experiences by allowing quick access to relevant content across diverse domains. Existing methods typically rely either on expensive manually labeled frame-level annotations or on a large external dataset of videos for weak supervision through category information. To overcome this we focus on unsupervised video highlight detection eliminating the need for manual annotations. We propose a novel unsupervised approach which capitalizes on the premise that significant moments tend to recur across multiple videos of the similar category in both audio and visual modalities. Surprisingly audio remains under-explored especially in unsupervised algorithms despite its potential to detect key moments. Through a clustering technique we identify pseudo-categories of videos and compute audio pseudo-highlight scores for each video by measuring the similarities of audio features among audio clips of all the videos within each pseudo-category. Similarly we also compute visual pseudo-highlight scores for each video using visual features. Then we combine audio and visual pseudo-highlights to create the audio-visual pseudo ground-truth highlight of each video for training an audio-visual highlight detection network. Extensive experiments and ablation studies on three benchmarks showcase the superior performance of our method over prior work.