Summary of Aidovecl: Ai-generated Dataset Of Outpainted Vehicles For Eye-level Classification and Localization, by Amir Kazemi et al.
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
by Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher Tessum
First submitted to arxiv on: 31 Oct 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 introduces a novel approach to address the issue of annotated data scarcity in computer vision, particularly relevant for autonomous driving, urban planning, and environmental monitoring. The authors leverage outpainting to generate artificial contexts and annotations, significantly reducing manual labeling efforts. They create a dataset comprising AI-generated vehicle images obtained by detecting and cropping vehicles from seed images, which are then outpainted onto larger canvases to simulate varied real-world conditions. The outpainted images include detailed annotations, providing high-quality ground truth data. Advanced outpainting techniques and image quality assessments ensure visual fidelity and contextual relevance. Experimental results show that augmentation with outpainted vehicles improves overall performance metrics by up to 8% and enhances prediction of underrepresented classes by up to 20%. This approach presents a solution that enhances dataset versatility across multiple domains of machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in computer vision where people have to spend a lot of time labeling images. They create new images with correct labels using a technique called outpainting, which makes it easier and faster to get the data needed for machine learning models. This is especially helpful for applications like autonomous driving, urban planning, and environmental monitoring, where there’s a lack of diverse vehicle images. The new approach shows that by using these generated images, performance can improve by up to 8% and predictions become more accurate. |
Keywords
* Artificial intelligence * Machine learning
