Learning Transformations To Reduce the Geometric Shift in Object Detection

Vidit Vidit, Martin Engilberge, Mathieu Salzmann

Abstract:

The performance of modern object detectors drops when
the test distribution differs from the training one. Most of
the methods that address this focus on object appearance
changes caused by, e.g., different illumination conditions,
or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in
the image capture process, or due to the constraints of the
environment causing differences in the apparent geometry
of the content itself. We introduce a self-training approach
that learns a set of geometric transformations to minimize
these shifts without leveraging any labeled data in the new
domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera’s field
of view (FoV) change and a viewpoint change. Our results
evidence that learning geometric transformations helps detectors to perform better in the target domains.