  Open-set object detection (OSOD) aims to detect the known categories and
reject unknown objects in a dynamic world, which has achieved significant
attention. However, previous approaches only consider this problem in
data-abundant conditions, while neglecting the few-shot scenes. In this paper,
we seek a solution for the generalized few-shot open-set object detection
(G-FOOD), which aims to avoid detecting unknown classes as known classes with a
high confidence score while maintaining the performance of few-shot detection.
The main challenge for this task is that few training samples induce the model
to overfit on the known classes, resulting in a poor open-set performance. We
propose a new G-FOOD algorithm to tackle this issue, named
\underline{F}ew-sh\underline{O}t \underline{O}pen-set \underline{D}etector
(FOOD), which contains a novel class weight sparsification classifier (CWSC)
and a novel unknown decoupling learner (UDL). To prevent over-fitting, CWSC
randomly sparses parts of the normalized weights for the logit prediction of
all classes, and then decreases the co-adaptability between the class and its
neighbors. Alongside, UDL decouples training the unknown class and enables the
model to form a compact unknown decision boundary. Thus, the unknown objects
can be identified with a confidence probability without any threshold,
prototype, or generation. We compare our method with several state-of-the-art
OSOD methods in few-shot scenes and observe that our method improves the
F-score of unknown classes by 4.80\%-9.08\% across all shots in VOC-COCO
dataset settings \footnote[1]{The source code is available at
\url{https://github.com/binyisu/food}}.
