Title: Generating Features with Increased Crop-Related Diversity for Few-shot Object Detection. Abstract: Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not perfectly contain the objects but overlap with them in many possible ways, exhibiting great variability induced by different object scales, object positions (w.r.t. the boxes), object parts, and backgrounds. Training a robust classifier against this 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 variability. The main idea is to transform the latent space such that latent codes with different norms represent different crop-related variations. This allows us to generate features with increased crop-related diversity via 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 crop-related variation. 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. Our experimental results show that our generated features consistently improve state-of-the-art few-shot object detection methods on PASCAL VOC and COCO datasets.