InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
Authors: name: Chengjian Feng, name: Yujie Zhong, name: Zequn Jie, name: Weidi Xie, name: Lin Ma

In this paper, we introduce a novel paradigm to enhance the ability of object
detector, e.g., expanding categories or improving detection performance, by
training on synthetic dataset generated from diffusion models. Specifically, we
integrate an instance-level grounding head into a pre-trained, generative
diffusion model, to augment it with the ability of localising arbitrary
instances in the generated images. The grounding head is trained to align the
text embedding of category names with the regional visual feature of the
diffusion model, using supervision from an off-the-shelf object detector, and a
novel self-training scheme on (novel) categories not covered by the detector.
This enhanced version of diffusion model, termed as InstaGen, can serve as a
data synthesizer for object detection. We conduct thorough experiments to show
that, object detector can be enhanced while training on the synthetic dataset
from InstaGen, demonstrating superior performance over existing
state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 to
5.2 AP) scenarios.