Summary of Quantifying the Synthetic and Real Domain Gap in Aerial Scene Understanding, by Alina Marcu
Quantifying the synthetic and real domain gap in aerial scene understanding
by Alina Marcu
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 addresses the gap between synthetic and real-world imagery to improve transformer-based models and datasets, particularly in underexplored domains like aerial scene understanding. The authors introduce Multi-Model Consensus Metric (MMCM) and depth-based structural metrics for scene complexity assessment, enabling robust evaluation of perceptual and structural disparities between domains. Experimental analysis using Dronescapes and Skyscenes datasets shows that real-world scenes exhibit higher consensus among state-of-the-art vision transformers, while synthetic scenes demonstrate greater variability, challenging model adaptability. The results highlight the need for enhanced simulation fidelity and model generalization, providing insights into domain characteristics and model performance, ultimately offering a pathway for improved domain adaptation strategies in aerial scene understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computer models can understand real-world images compared to fake ones. They want to improve these models and the datasets they’re trained on, especially when it comes to understanding aerial scenes like landscapes from airplanes. To do this, they developed a new way to measure how similar or different two images are, based on things like depth and structure. When they tested their method using real and fake images, they found that real-world images are generally easier for the models to understand than fake ones, which can be tricky to adapt to. This research helps us understand why this is the case and how we can make computer models better at understanding aerial scenes. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Scene understanding » Transformer
