  This paper addresses the problem of common object detection, which aims to
detect objects of similar categories from a set of images. Although it shares
some similarities with the standard object detection and co-segmentation,
common object detection, recently promoted by \cite{Jiang2019a}, has some
unique advantages and challenges. First, it is designed to work on both
closed-set and open-set conditions, a.k.a. known and unknown objects. Second,
it must be able to match objects of the same category but not restricted to the
same instance, texture, or posture. Third, it can distinguish multiple objects.
In this work, we introduce the Single Stage Common Object Detection (SSCOD) to
detect class-agnostic common objects from an image set. The proposed method is
built upon the standard single-stage object detector. Furthermore, an embedded
branch is introduced to generate the object's representation feature, and their
similarity is measured by cosine distance. Experiments are conducted on PASCAL
VOC 2007 and COCO 2014 datasets. While being simple and flexible, our proposed
SSCOD built upon ATSSNet performs significantly better than the baseline of the
standard object detection, while still be able to match objects of unknown
categories. Our source code can be found at
\href{https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection}{(URL)}
