CVPR 2023 - Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

In this episode we discuss Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection by Jingyi Xu, Hieu Le, Dimitris Samaras. The paper proposes a novel data generation model, based on a variational autoencoder (VAE), for training robust object detectors in few-shot settings. The model is designed to generate crops with increased crop-related diversity to account for the variability in object proposals generated by two-stage detectors. By transforming the latent space, the model produces features with diverse difficulty levels by varying the latent norm, which is rescaled based on the intersection-over-union (IoU) score of the input crop with respect to the ground-truth box. The experiments show that the generated features consistently improve state-of-the-art few-shot object detection methods on PASCAL VOC and MS COCO datasets.