  Although modern object detectors rely heavily on a significant amount of
training data, humans can easily detect novel objects using a few training
examples. The mechanism of the human visual system is to interpret spatial
relationships among various objects and this process enables us to exploit
contextual information by considering the co-occurrence of objects. Thus, we
propose a spatial reasoning framework that detects novel objects with only a
few training examples in a context. We infer geometric relatedness between
novel and base RoIs (Region-of-Interests) to enhance the feature representation
of novel categories using an object detector well trained on base categories.
We employ a graph convolutional network as the RoIs and their relatedness are
defined as nodes and edges, respectively. Furthermore, we present spatial data
augmentation to overcome the few-shot environment where all objects and
bounding boxes in an image are resized randomly. Using the PASCAL VOC and MS
COCO datasets, we demonstrate that the proposed method significantly
outperforms the state-of-the-art methods and verify its efficacy through
extensive ablation studies.
