  We propose a new paradigm to automatically generate training data with
accurate labels at scale using the text-to-image synthesis frameworks (e.g.,
DALL-E, Stable Diffusion, etc.). The proposed approach1 decouples training data
generation into foreground object generation, and contextually coherent
background generation. To generate foreground objects, we employ a
straightforward textual template, incorporating the object class name as input
prompts. This is fed into a text-to-image synthesis framework, producing
various foreground images set against isolated backgrounds. A
foreground-background segmentation algorithm is then used to generate
foreground object masks. To generate context images, we begin by creating
language descriptions of the context. This is achieved by applying an image
captioning method to a small set of images representing the desired context.
These textual descriptions are then transformed into a diverse array of context
images via a text-to-image synthesis framework. Subsequently, we composite
these with the foreground object masks produced in the initial step, utilizing
a cut-and-paste method, to formulate the training data. We demonstrate the
advantages of our approach on five object detection and segmentation datasets,
including Pascal VOC and COCO. We found that detectors trained solely on
synthetic data produced by our method achieve performance comparable to those
trained on real data (Fig. 1). Moreover, a combination of real and synthetic
data yields even much better results. Further analysis indicates that the
synthetic data distribution complements the real data distribution effectively.
Additionally, we emphasize the compositional nature of our data generation
approach in out-of-distribution and zero-shot data generation scenarios. We
open-source our code at https://github.com/gyhandy/Text2Image-for-Detection
