Summary of Videosage: Video Summarization with Graph Representation Learning, by Jose M. Rojas Chaves et al.
VideoSAGE: Video Summarization with Graph Representation Learning
by Jose M. Rojas Chaves, Subarna Tripathi
First submitted to arxiv on: 14 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 A graph-based representation learning framework for video summarization is proposed. The approach converts an input video into a graph where nodes correspond to individual frames, imposing sparsity by connecting only nearby frames. This is formulated as a binary node classification problem, classifying frames for inclusion in the output summary video. Experiments on SumMe and TVSum datasets demonstrate the effectiveness of this nimble model, outperforming state-of-the-art approaches while being one order of magnitude more efficient in compute time and memory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video summarization gets a boost with a new graph-based approach that converts frames into nodes and connects nearby ones. This helps capture long-range interactions between frames, and makes the model more efficient to train. The results show this method works well on two datasets, SumMe and TVSum, outperforming existing approaches while being faster and using less memory. |
Keywords
» Artificial intelligence » Classification » Representation learning » Summarization
