  Deep learning based object detectors are commonly deployed on mobile devices
to solve a variety of tasks. For maximum accuracy, each detector is usually
trained to solve one single specific task, and comes with a completely
independent set of parameters. While this guarantees high performance, it is
also highly inefficient, as each model has to be separately downloaded and
stored. In this paper we address the question: can task-specific detectors be
trained and represented as a shared set of weights, plus a very small set of
additional weights for each task? The main contributions of this paper are the
following: 1) we perform the first systematic study of parameter-efficient
transfer learning techniques for object detection problems; 2) we propose a
technique to learn a model patch with a size that is dependent on the
difficulty of the task to be learned, and validate our approach on 10 different
object detection tasks. Our approach achieves similar accuracy as previously
proposed approaches, while being significantly more compact.
