  Privacy and memory are two recurring themes in a broad conversation about the
societal impact of AI. These concerns arise from the need for huge amounts of
data to train deep neural networks. A promise of Generalized Few-shot Object
Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for
collecting abundant training samples of novel classes we wish to detect by
leveraging prior knowledge from old classes (i.e., base classes). G-FSOD
strives to learn these novel classes while alleviating catastrophic forgetting
of the base classes. However, existing approaches assume that the base images
are accessible, an assumption that does not hold when sharing and storing data
is problematic. In this work, we propose the first data-free knowledge
distillation (DFKD) approach for G-FSOD that leverages the statistics of the
region of interest (RoI) features from the base model to forge instance-level
features without accessing the base images. Our contribution is three-fold: (1)
we design a standalone lightweight generator with (2) class-wise heads (3) to
generate and replay diverse instance-level base features to the RoI head while
finetuning on the novel data. This stands in contrast to standard DFKD
approaches in image classification, which invert the entire network to generate
base images. Moreover, we make careful design choices in the novel finetuning
pipeline to regularize the model. We show that our approach can dramatically
reduce the base memory requirements, all while setting a new standard for
G-FSOD on the challenging MS-COCO and PASCAL-VOC benchmarks.
