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In the recent years, the field of object detection has experienced a major breakthrough after the popularization of the Deep Learning paradigm. Approaches such as YOLO, SSD or FasterRCNN hold the state of the art in the general task of object detection [1].

However, in the specific application scenario where we are given only one reference image for the object/logo we want to detect, deep learning-based methods seem to be less applicable and local feature descriptors such as SIFT and SURF appear as more suitable alternatives, with a near-zero deployment cost.

My question is, can you point out some application strategies (preferably with implementations available rather than just research papers describing them) where Deep Learning is successfully used for object detection with just one training image per object class?

Example application scenario:

In this case, SIFT successfully detects the logo in the image: