  Few-shot detection is a major task in pattern recognition which seeks to
localize objects using models trained with few labeled data. One of the
mainstream few-shot methods is transfer learning which consists in pretraining
a detection model in a source domain prior to its fine-tuning in a target
domain. However, it is challenging for fine-tuned models to effectively
identify new classes in the target domain, particularly when the underlying
labeled training data are scarce. In this paper, we devise a novel sparse
context transformer (SCT) that effectively leverages object knowledge in the
source domain, and automatically learns a sparse context from only few training
images in the target domain. As a result, it combines different relevant clues
in order to enhance the discrimination power of the learned detectors and
reduce class confusion. We evaluate the proposed method on two challenging
few-shot object detection benchmarks, and empirical results show that the
proposed method obtains competitive performance compared to the related
state-of-the-art.
