Summary of Enhancing Video Summarization with Context Awareness, by Hai-dang Huynh-lam et al.
Enhancing Video Summarization with Context Awareness
by Hai-Dang Huynh-Lam, Ngoc-Phuong Ho-Thi, Minh-Triet Tran, Trung-Nghia Le
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Video summarization is a crucial machine learning problem aiming to efficiently browse and retrieve relevant information from vast amounts of video content. To improve applications like video surveillance, education, entertainment, and social media, this research area automatically generates concise summaries by selecting keyframes, shots, or segments that capture the video’s essence. Despite its importance, video summarization lacks diverse datasets and comprehensive evaluation metrics, hindering algorithm assessment and progress. This paper proposes an unsupervised approach using video structure and information to generate informative summaries without fixed annotations. It introduces a human-centric evaluation pipeline assessing summary informativeness through comparisons with ground truth summaries. The framework outperforms existing unsupervised approaches and achieves competitive results compared to state-of-the-art supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video summarization is a way to make it easier to find important parts in lots of videos. This helps us do things like watch surveillance footage, learn new things, have fun watching movies, or see what’s trending on social media. But we need better ways to decide which parts of the video are most important and to figure out if our methods are working well. To solve this problem, researchers came up with a new way to make summaries without needing any special training data. They also created a new way to test how good their summaries are by asking people what they think is the most important part of the video. This new approach does better than some other ways that don’t need training and is almost as good as methods that do need training. |
Keywords
» Artificial intelligence » Machine learning » Summarization » Supervised » Unsupervised
