Few-shot logo detection

The proliferation of deep learning has driven research into deep learning-based logo detection, which usually needs a large number of annotated data to train the model. However, due to the occasional appearance of new brands or the high cost of annotation, the number of training data is limited. Against this backdrop, the authors adapt the few-shot object detection into logo detection, and thus present a cutting-edge method called Double Classification Head (DCH) for Few-Shot Logo Detection (DCH-FSLogo), which aims at detecting the unseen logo classes using few annotated data. Unlike the traditional few-shot detection, some logo objects are similar to their backgrounds and have diverse shapes as well. For this reason, the authors adopt balanced feature pyramid and deformable Region of Interest pooling in DCH-FSLogo, this enhances the feature extraction capability and adapts to the different logo shapes. In addition, we introduce the DCH for few-shot logo detection to detect logo objects using few annotated data. Specifically, we use an extra classification head for the base classes to ease the influence from the novel classes. The experimental results on four datasets, namely: FlickrLogos-32, FoodLogoDet-1500-100, LogoDet-3K-100 and QMUL-OpenLogo-100, demonstrate that our method achieves better performance.