Recycle deep features for better object detection
This work addresses the need for efficient object detection in various applications with limited data, though it appears incremental as it builds on existing algorithms and architectures.
The paper tackles the problem of improving object detection performance without needing large training datasets by proposing a multi-stage class-agnostic pipeline that recycles deep features. It demonstrates feasibility on a dataset with ~1200 samples, achieving better detection through a novel network architecture.
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.