تقرير
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
| العنوان: | DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection |
|---|---|
| المؤلفون: | Zhang, Manlin, Wu, Jie, Ren, Yuxi, Li, Ming, Qin, Jie, Xiao, Xuefeng, Liu, Wei, Wang, Rui, Zheng, Min, Ma, Andy J. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scaling up detection-oriented data often require manual collection or generative models to obtain target images, followed by data augmentation and labeling to produce training pairs, which are costly, complex, or lacking diversity. To address these issues, we presentDiffusionEngine (DE), a data scaling-up engine that provides high-quality detection-oriented training pairs in a single stage. DE consists of a pre-trained diffusion model and an effective Detection-Adapter, contributing to generating scalable, diverse and generalizable detection data in a plug-and-play manner. Detection-Adapter is learned to align the implicit semantic and location knowledge in off-the-shelf diffusion models with detection-aware signals to make better bounding-box predictions. Additionally, we contribute two datasets, i.e., COCO-DE and VOC-DE, to scale up existing detection benchmarks for facilitating follow-up research. Extensive experiments demonstrate that data scaling-up via DE can achieve significant improvements in diverse scenarios, such as various detection algorithms, self-supervised pre-training, data-sparse, label-scarce, cross-domain, and semi-supervised learning. For example, when using DE with a DINO-based adapter to scale up data, mAP is improved by 3.1% on COCO, 7.6% on VOC, and 11.5% on Clipart. Comment: Code and Models are publicly available. Project Page: https://mettyz.github.io/DiffusionEngine |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2309.03893 |
| رقم الأكسشن: | edsarx.2309.03893 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |