  Few-shot object detection, the problem of modelling novel object detection
categories with few training instances, is an emerging topic in the area of
few-shot learning and object detection. Contemporary techniques can be divided
into two groups: fine-tuning based and meta-learning based approaches. While
meta-learning approaches aim to learn dedicated meta-models for mapping samples
to novel class models, fine-tuning approaches tackle few-shot detection in a
simpler manner, by adapting the detection model to novel classes through
gradient based optimization. Despite their simplicity, fine-tuning based
approaches typically yield competitive detection results. Based on this
observation, we focus on the role of loss functions and augmentations as the
force driving the fine-tuning process, and propose to tune their dynamics
through meta-learning principles. The proposed training scheme, therefore,
allows learning inductive biases that can boost few-shot detection, while
keeping the advantages of fine-tuning based approaches. In addition, the
proposed approach yields interpretable loss functions, as opposed to highly
parametric and complex few-shot meta-models. The experimental results highlight
the merits of the proposed scheme, with significant improvements over the
strong fine-tuning based few-shot detection baselines on benchmark Pascal VOC
and MS-COCO datasets, in terms of both standard and generalized few-shot
performance metrics.
