Summary of Towards Generalizable Scene Change Detection, by Jaewoo Kim et al.
Towards Generalizable Scene Change Detection
by Jaewoo Kim, Uehwan Kim
First submitted to arxiv on: 10 Sep 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 The proposed Generalizable Scene Change Detection Framework (GeSCF) aims to address the limitations of current state-of-the-art Scene Change Detection (SCD) approaches, which struggle with unseen environments and temporal conditions. GeSCF leverages the pre-trained Segment Anything Model (SAM) in a zero-shot manner, incorporating initial pseudo-mask generation and geometric-semantic mask matching techniques. The framework is evaluated using novel metrics and an evaluation protocol on the Generalizable Scene Change Detection (GeSCD) benchmark. Experimental results show that GeSCF achieves an average performance gain of 19.2% on existing SCD datasets and 30.0% on the newly introduced ChangeVPR dataset, nearly doubling prior art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to detect changes in scenes, like buildings or roads. Current methods work well when they’re trained on specific data, but they don’t generalize as well to new situations. The authors introduce a new framework that uses pre-trained models and special techniques to improve the detection of scene changes. They test this framework on several datasets and show that it performs better than current methods. |
Keywords
» Artificial intelligence » Mask » Sam » Zero shot
