  Many meta-learning methods are proposed for few-shot detection. However,
previous most methods have two main problems, poor detection APs, and strong
bias because of imbalance and insufficient datasets. Previous works mainly
alleviate these issues by additional datasets, multi-relation attention
mechanisms and sub-modules. However, they require more cost. In this work, for
meta-learning, we find that the main challenges focus on related or irrelevant
semantic features between categories. Therefore, based on semantic features, we
propose a Top-C classification loss (i.e., TCL-C) for classification task and a
category-based grouping mechanism for category-based meta-features obtained by
the meta-model. The TCL-C exploits the true-label prediction and the most
likely C-1 false classification predictions to improve detection performance on
few-shot classes. According to similar appearance (i.e., visual appearance,
shape, and limbs etc.) and environment in which objects often appear, the
category-based grouping mechanism splits categories into disjoint groups to
make similar semantic features more compact between categories within a group
and obtain more significant difference between groups, alleviating the strong
bias problem and further improving detection APs. The whole training consists
of the base model and the fine-tuning phases. According to grouping mechanism,
we group the meta-features vectors obtained by meta-model, so that the
distribution difference between groups is obvious, and the one within each
group is less. Extensive experiments on Pascal VOC dataset demonstrate that
ours which combines the TCL-C with category-based grouping significantly
outperforms previous state-of-the-art methods for few-shot detection. Compared
with previous competitive baseline, ours improves detection APs by almost 4%
for few-shot detection.
