  In the past decade, object detection tasks are defined mostly by large public
datasets. However, building object detection datasets is not scalable due to
inefficient image collecting and labeling. Furthermore, most labels are still
in the form of bounding boxes, which provide much less information than the
real human visual system. In this paper, we present a method to synthesize
object-in-scene images, which can preserve the objects' detailed features
without bringing irrelevant information. In brief, given a set of images
containing a target object, our algorithm first trains a model to find an
approximate center of the object as an anchor, then makes an outline regression
to estimate its boundary, and finally blends the object into a new scene. Our
result shows that in the synthesized image, the boundaries of objects blend
very well with the background. Experiments also show that SOTA segmentation
models work well with our synthesized data.
