  Human beings not only have the ability to recognize novel unseen classes, but
also can incrementally incorporate the new classes to existing knowledge
preserved. However, zero-shot learning models assume that all seen classes
should be known beforehand, while incremental learning models cannot recognize
unseen classes. This paper introduces a novel and challenging task of
Incrementally Zero-Shot Detection (IZSD), a practical strategy for both
zero-shot learning and class-incremental learning in real-world object
detection. An innovative end-to-end model -- IZSD-EVer was proposed to tackle
this task that requires incrementally detecting new classes and detecting the
classes that have never been seen. Specifically, we propose a novel extreme
value analyzer to detect objects from old seen, new seen, and unseen classes,
simultaneously. Additionally and technically, we propose two innovative losses,
i.e., background-foreground mean squared error loss alleviating the extreme
imbalance of the background and foreground of images, and projection distance
loss aligning the visual space and semantic spaces of old seen classes.
Experiments demonstrate the efficacy of our model in detecting objects from
both the seen and unseen classes, outperforming the alternative models on
Pascal VOC and MSCOCO datasets.
