Summary of Cluster-based Video Summarization with Temporal Context Awareness, by Hai-dang Huynh-lam et al.
Cluster-based Video Summarization with Temporal 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 This paper presents TAC-SUM, a novel approach to video summarization that doesn’t require training. The method addresses the limitations of existing cluster-based models by incorporating temporal context. It partitions the input video into segments with clustering information, allowing for the injection of temporal awareness into the clustering process. The resulting clusters are then used to compute the final summary using simple rules for keyframe selection and frame importance scoring. Experimental results on the SumMe dataset show that TAC-SUM outperforms existing unsupervised methods and achieves comparable performance to state-of-the-art supervised summarization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to summarize videos without needing to train a model first. It’s better than other approaches because it understands when events happen in the video, like people walking into a room or an explosion happening. The method breaks down the video into smaller parts and then uses those parts to make the summary. The results show that this approach is really good at summarizing videos, even beating some more complicated methods. |
Keywords
» Artificial intelligence » Clustering » Summarization » Supervised » Unsupervised
