  Many emerging applications of intelligent robots need to explore and
understand new environments, where it is desirable to detect objects of novel
classes on the fly with minimum online efforts. This is an object detection on
demand (ODOD) task. It is challenging, because it is impossible to annotate a
large number of data on the fly, and the embedded systems are usually unable to
perform back-propagation which is essential for training. Most existing
few-shot detection methods are confronted here as they need extra training. We
propose a novel morphable detector (MD), that simply "morphs" some of its
changeable parameters online estimated from the few samples, so as to detect
novel classes without any extra training. The MD has two sets of parameters,
one for the feature embedding and the other for class representation (called
"prototypes"). Each class is associated with a hidden prototype to be learned
by integrating the visual and semantic embeddings. The learning of the MD is
based on the alternate learning of the feature embedding and the prototypes in
an EM-like approach which allows the recovery of an unknown prototype from a
few samples of a novel class. Once an MD is learned, it is able to use a few
samples of a novel class to directly compute its prototype to fulfill the
online morphing process. We have shown the superiority of the MD in Pascal,
COCO and FSOD datasets. The code is available
https://github.com/Zhaoxiangyun/Morphable-Detector.
