  Aiming at improving the performance of existing detection algorithms
developed for different applications, we propose a region regression-based
multi-stage class-agnostic detection pipeline, whereby the existing algorithms
are employed for providing the initial detection proposals. Better detection is
obtained by exploiting the power of deep learning in the region regress scheme
while avoiding the requirement on a huge amount of reference data for training
deep neural networks. Additionally, a novel network architecture with recycled
deep features is proposed, which provides superior regression results compared
to the commonly used architectures. As demonstrated on a data set with ~1200
samples of different classes, it is feasible to successfully train a deep
neural network in our proposed architecture and use it to obtain the desired
detection performance. Since only slight modifications are required to common
network architectures and since the deep neural network is trained using the
standard hyperparameters, the proposed detection is well accessible and can be
easily adopted to a broad variety of detection tasks.
