  Few-shot object detection (FSOD), an efficient method for addressing the
severe data-hungry problem, has been extensively discussed. Current works have
significantly advanced the problem in terms of model and data. However, the
overall performance of most FSOD methods still does not fulfill the desired
accuracy. In this paper we improve the FSOD model to address the severe issue
of sample imbalance and weak feature propagation. To alleviate modeling bias
from data-sufficient base classes, we examine the effect of decoupling the
parameters for classes with sufficient data and classes with few samples in
various ways. We design a base-novel categories decoupled DETR (DeDETR) for
FSOD. We also explore various types of skip connection between the encoder and
decoder for DETR. Besides, we notice that the best outputs could come from the
intermediate layer of the decoder instead of the last layer; therefore, we
build a unified decoder module that could dynamically fuse the decoder layers
as the output feature. We evaluate our model on commonly used datasets such as
PASCAL VOC and MSCOCO. Our results indicate that our proposed module could
achieve stable improvements of 5% to 10% in both fine-tuning and meta-learning
paradigms and has outperformed the highest score in recent works.
