Two-stage object detectors generate object proposals and classify them to
detect objects in images. These proposals often do not contain the objects
perfectly but overlap with them in many possible ways, exhibiting great
variability in the difficulty levels of the proposals. Training a robust
classifier against this crop-related variability requires abundant training
data, which is not available in few-shot settings. To mitigate this issue, we
propose a novel variational autoencoder (VAE) based data generation model,
which is capable of generating data with increased crop-related diversity. The
main idea is to transform the latent space such latent codes with different
norms represent different crop-related variations. This allows us to generate
features with increased crop-related diversity in difficulty levels by simply
varying the latent norm. In particular, each latent code is rescaled such that
its norm linearly correlates with the IoU score of the input crop w.r.t. the
ground-truth box. Here the IoU score is a proxy that represents the difficulty
level of the crop. We train this VAE model on base classes conditioned on the
semantic code of each class and then use the trained model to generate features
for novel classes. In our experiments our generated features consistently
improve state-of-the-art few-shot object detection methods on the PASCAL VOC
and MS COCO datasets.

该论文提出了一种基于变分自编码器 (VAE) 的数据生成模型，以增加图像中物体提议的多样性，缓解了训练鲁棒分类器方面的问题，并在 PASCAL VOC 和 MS COCO 数据集上显著提高了少量训练样本情况下的物体检测性能。