Gen2Det: Generate to Detect
CoRR(2023)
摘要
Recently diffusion models have shown improvement in synthetic image quality
as well as better control in generation. We motivate and present Gen2Det, a
simple modular pipeline to create synthetic training data for object detection
for free by leveraging state-of-the-art grounded image generation methods.
Unlike existing works which generate individual object instances, require
identifying foreground followed by pasting on other images, we simplify to
directly generating scene-centric images. In addition to the synthetic data,
Gen2Det also proposes a suite of techniques to best utilize the generated data,
including image-level filtering, instance-level filtering, and better training
recipe to account for imperfections in the generation. Using Gen2Det, we show
healthy improvements on object detection and segmentation tasks under various
settings and agnostic to detection methods. In the long-tailed detection
setting on LVIS, Gen2Det improves the performance on rare categories by a large
margin while also significantly improving the performance on other categories,
e.g. we see an improvement of 2.13 Box AP and 1.84 Mask AP over just training
on real data on LVIS with Mask R-CNN. In the low-data regime setting on COCO,
Gen2Det consistently improves both Box and Mask AP by 2.27 and 1.85 points. In
the most general detection setting, Gen2Det still demonstrates robust
performance gains, e.g. it improves the Box and Mask AP on COCO by 0.45 and
0.32 points.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要