Summary of A Simple Background Augmentation Method For Object Detection with Diffusion Model, by Yuhang Li et al.
A Simple Background Augmentation Method for Object Detection with Diffusion Model
by Yuhang Li, Xin Dong, Chen Chen, Weiming Zhuang, Lingjuan Lyu
First submitted to arxiv on: 1 Aug 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 data augmentation approach leverages generative models like Stable Diffusion for text-to-image synthesis to enhance dataset diversity, benefiting downstream tasks such as object detection and instance segmentation. The method focuses on generating variations of labeled real images using inpainting-based object and background augmentation, without requiring additional annotations. Background augmentation is found to significantly improve model robustness and generalization capabilities. Evaluations on the COCO dataset and other key object detection benchmarks demonstrate notable enhancements in model performance across diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in computer vision where not having enough diverse data hurts how well models work. The researchers propose a simple way to make the training data more diverse using generative models that can create fake images based on text prompts. They generate new versions of real images with different objects and backgrounds, without needing extra labels. This helps models do better in different situations. The results show that this approach makes models more robust and able to work well on unseen data. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion » Generalization » Image synthesis » Instance segmentation » Object detection
