Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence
(2407.13933)Abstract
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 significantly 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 an innovative 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. Subsequently, 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 highlight detection benchmarks showcase the superior performance of our method over prior work.
Overview
-
The paper introduces an unsupervised method for detecting video highlights using audio and visual data, eliminating the need for annotated datasets.
-
The method leverages audio-visual feature extraction, self-supervised learning, and temporal consistency checks to identify significant video segments.
-
Experimental results show that the proposed approach outperforms existing unsupervised methods and rivals supervised models, highlighting its potential for various applications like sports analytics and content curation.
Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence
The paper, authored by Zahidul Islam, Sujoy Paul, and Mrigank Rochan, presents an innovative method for unsupervised video highlight detection by leveraging both audio and visual data. This approach addresses the challenge of identifying significant segments within videos without relying on annotated datasets, which are often labor-intensive and costly to obtain.
Methodology
The proposed methodology utilizes a combination of audio-visual recurrence learning to detect highlights. This approach is rooted in the premise that noteworthy moments in videos exhibit recurring patterns in both audio and visual modalities. The researchers developed a model that learns these patterns without supervised labels by exploiting the following key components:
- Audio-Visual Feature Extraction: The model extracts features from both audio and visual streams. Using convolutional neural networks (CNN) and audio feature extractors, the framework captures high-level semantic features from both modalities.
- Self-Supervised Learning: This component leverages the recurrence of patterns within and across the audio and visual modalities. The model is trained to learn from these patterns, identifying features that are indicative of highlights.
- Temporal Consistency Check: To enhance reliability, the approach incorporates a temporal consistency check where the recurrence of features over time is scrutinized. This helps in distinguishing genuine highlights from fleeting or irrelevant events.
Experimental Results
The authors conducted extensive experiments to validate their approach. The results demonstrate notable performance improvements over state-of-the-art unsupervised methods. Key numerical results include:
- Precision and Recall: The proposed method yielded precision and recall values that significantly surpassed existing benchmarks, indicating its efficacy in accurately detecting video highlights without supervised data.
- F1-Score: The F1-score, a balanced measure of precision and recall, also showed considerable gains, underscoring the robustness of the model.
In a comparative analysis, this unsupervised method was found to rival, and in some cases exceed, the performance of supervised models, highlighting its potential applicability across various domains where annotated data is scarce.
Implications and Future Directions
The implications of this research are multifaceted, impacting both practical applications and theoretical advancements. Practically, the method can be applied to a variety of fields including sports analytics, content curation, and video summarization. The ability to automatically identify highlights can save considerable resources and streamline workflows in media-related industries.
Theoretically, this work opens new avenues in the field of unsupervised learning, specifically in multimodal data. The success of the audio-visual recurrence approach suggests that similar methods could be generalized to other tasks involving temporal and multimodal data.
Future developments may explore deeper integration of more complex neural architectures, such as transformers, to further enhance the capture of long-range dependencies and interactions between audio and visual features. Additionally, extending this approach to real-time applications could be an intriguing direction, potentially transforming live event broadcasting and surveillance systems.
Conclusion
This paper presents a compelling advancement in the domain of video highlight detection, demonstrating that unsupervised learning methods can achieve high performance by exploiting the recurrence of audio-visual patterns. The research findings not only enhance our understanding of unsupervised approaches but also provide a robust framework for practical applications where labeled data is limited or unavailable.
Create an account to read this summary for free: