Summary of Improving Object Detector Training on Synthetic Data by Starting with a Strong Baseline Methodology, By Frank A. Ruis and Alma M. Liezenga and Friso G. Heslinga and Luca Ballan and Thijs A. Eker and Richard J. M. Den Hollander and Martin C. Van Leeuwen and Judith Dijk and Wyke Huizinga
Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology
by Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga, Luca Ballan, Thijs A. Eker, Richard J. M. den Hollander, Martin C. van Leeuwen, Judith Dijk, Wyke Huizinga
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 proposes a methodology to improve the performance of pre-trained object detectors when training on synthetic data. The authors focus on extracting salient information from synthetic data without forgetting useful features learned from pre-training on real images. They incorporate data augmentation methods and a Transformer backbone, achieving relatively strong performance without specialized synthetic data transfer methods. The approach is tested on several datasets, including RarePlanes, DGTA-VisDrone, and an in-house vehicle detection dataset, with near-perfect performance achieved on the latter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps object detectors work better with fake data. Collecting real data can be hard or expensive, so training models on fake data could be a solution. But there’s a problem: fake data doesn’t look like real data, making it hard for models to perform well. The authors found that using pre-trained models and adding some extra steps can help improve performance when training on synthetic data. They tested their approach on several datasets and got good results. |
Keywords
» Artificial intelligence » Data augmentation » Synthetic data » Transformer
