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.

翻译：两阶段目标检测器通过生成目标提议并对它们进行分类来检测图像中的目标。这些提议通常无法完美包含目标，而是以多种可能方式与目标重叠，在提议的难易程度上表现出巨大差异。针对这种与裁剪相关的变异性训练鲁棒分类器需要大量训练数据，而在小样本场景中这一条件难以满足。为缓解此问题，我们提出一种基于变分自编码器（VAE）的新型数据生成模型，该模型能够生成具有更高裁剪相关多样性的数据。核心思想是通过变换潜在空间，使具有不同范数的潜在编码对应不同的裁剪相关变异。这样就能通过简单改变潜在范数，生成难易程度具有更高裁剪相关多样性的特征。具体而言，每个潜在编码被重新缩放，使其范数与输入裁剪相对于真实标注框的交并比（IoU）分数呈线性相关，其中IoU分数代表裁剪的难易程度代理指标。我们在基类上以每个类别的语义编码为条件训练此VAE模型，随后利用训练好的模型为新类生成特征。实验中，我们生成的特征在PASCAL VOC和MS COCO数据集上持续改进了现有最优小样本目标检测方法的性能。