  Despite increasing efforts on universal representations for visual
recognition, few have addressed object detection. In this paper, we develop an
effective and efficient universal object detection system that is capable of
working on various image domains, from human faces and traffic signs to medical
CT images. Unlike multi-domain models, this universal model does not require
prior knowledge of the domain of interest. This is achieved by the introduction
of a new family of adaptation layers, based on the principles of squeeze and
excitation, and a new domain-attention mechanism. In the proposed universal
detector, all parameters and computations are shared across domains, and a
single network processes all domains all the time. Experiments, on a newly
established universal object detection benchmark of 11 diverse datasets, show
that the proposed detector outperforms a bank of individual detectors, a
multi-domain detector, and a baseline universal detector, with a 1.3x parameter
increase over a single-domain baseline detector. The code and benchmark will be
released at http://www.svcl.ucsd.edu/projects/universal-detection/.
