  This paper is on Few-Shot Object Detection (FSOD), where given a few
templates (examples) depicting a novel class (not seen during training), the
goal is to detect all of its occurrences within a set of images. From a
practical perspective, an FSOD system must fulfil the following desiderata: (a)
it must be used as is, without requiring any fine-tuning at test time, (b) it
must be able to process an arbitrary number of novel objects concurrently while
supporting an arbitrary number of examples from each class and (c) it must
achieve accuracy comparable to a closed system. Towards satisfying (a)-(c), in
this work, we make the following contributions: We introduce, for the first
time, a simple, yet powerful, few-shot detection transformer (FS-DETR) based on
visual prompting that can address both desiderata (a) and (b). Our system
builds upon the DETR framework, extending it based on two key ideas: (1) feed
the provided visual templates of the novel classes as visual prompts during
test time, and (2) ``stamp'' these prompts with pseudo-class embeddings (akin
to soft prompting), which are then predicted at the output of the decoder.
Importantly, we show that our system is not only more flexible than existing
methods, but also, it makes a step towards satisfying desideratum (c).
Specifically, it is significantly more accurate than all methods that do not
require fine-tuning and even matches and outperforms the current
state-of-the-art fine-tuning based methods on the most well-established
benchmarks (PASCAL VOC & MSCOCO).
