  Few-shot object detection (FSOD) aims to strengthen the performance of novel
object detection with few labeled samples. To alleviate the constraint of few
samples, enhancing the generalization ability of learned features for novel
objects plays a key role. Thus, the feature learning process of FSOD should
focus more on intrinsical object characteristics, which are invariant under
different visual changes and therefore are helpful for feature generalization.
Unlike previous attempts of the meta-learning paradigm, in this paper, we
explore how to smooth object features with intrinsical characteristics that are
universal across different object categories. We propose a new prototype,
namely universal prototype, that is learned from all object categories. Besides
the advantage of characterizing invariant characteristics, the universal
prototypes alleviate the impact of unbalanced object categories. After
augmenting object features with the universal prototypes, we impose a
consistency loss to maximize the agreement between the augmented features and
the original one, which is beneficial for learning invariant object
characteristics. Thus, we develop a new framework of few-shot object detection
with universal prototypes (${FSOD}^{up}$) that owns the merit of feature
generalization towards novel objects. Experimental results on PASCAL VOC and MS
COCO demonstrate the effectiveness of ${FSOD}^{up}$. Particularly, for the
1-shot case of VOC Split2, ${FSOD}^{up}$ outperforms the baseline by 6.8\% in
terms of mAP. Moreover, we further verify ${FSOD}^{up}$ on a long-tail
detection dataset, i.e., LVIS. And employing ${FSOD}^{up}$ outperforms the
state-of-the-art method.
