  Few-shot object detection (FSOD) is to detect objects with a few examples.
However, existing FSOD methods do not consider hierarchical fine-grained
category structures of objects that exist widely in real life. For example,
animals are taxonomically classified into orders, families, genera and species
etc. In this paper, we propose and solve a new problem called hierarchical
few-shot object detection (Hi-FSOD), which aims to detect objects with
hierarchical categories in the FSOD paradigm. To this end, on the one hand, we
build the first large-scale and high-quality Hi-FSOD benchmark dataset
HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432
categories. All the categories are organized into a 4-level taxonomy,
consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the
other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical
contrastive learning approach is developed to constrain the feature space so
that the feature distribution of objects is consistent with the hierarchical
taxonomy and the model's generalization power is strengthened. Meanwhile, a
probabilistic loss is designed to enable the child nodes to correct the
classification errors of their parent nodes in the taxonomy. Extensive
experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL
outperforms the existing FSOD methods.
