  Object detection has achieved substantial progress in the last decade.
However, detecting novel classes with only few samples remains challenging,
since deep learning under low data regime usually leads to a degraded feature
space. Existing works employ a holistic fine-tuning paradigm to tackle this
problem, where the model is first pre-trained on all base classes with abundant
samples, and then it is used to carve the novel class feature space.
Nonetheless, this paradigm is still imperfect. Durning fine-tuning, a novel
class may implicitly leverage the knowledge of multiple base classes to
construct its feature space, which induces a scattered feature space, hence
violating the inter-class separability. To overcome these obstacles, we propose
a two-step fine-tuning framework, Few-shot object detection via Association and
DIscrimination (FADI), which builds up a discriminative feature space for each
novel class with two integral steps. 1) In the association step, in contrast to
implicitly leveraging multiple base classes, we construct a compact novel class
feature space via explicitly imitating a specific base class feature space.
Specifically, we associate each novel class with a base class according to
their semantic similarity. After that, the feature space of a novel class can
readily imitate the well-trained feature space of the associated base class. 2)
In the discrimination step, to ensure the separability between the novel
classes and associated base classes, we disentangle the classification branches
for base and novel classes. To further enlarge the inter-class separability
between all classes, a set-specialized margin loss is imposed. Extensive
experiments on Pascal VOC and MS-COCO datasets demonstrate FADI achieves new
SOTA performance, significantly improving the baseline in any shot/split by
+18.7. Notably, the advantage is most announced on extremely few-shot
scenarios.
