Improving Few-Shot and Cross-Domain Object Detection on Aerial Images with a Diffusion-Based Detector

Object Detection models are difficult to adapt to real use cases as they require large and expensive datasets during training. Few-Shot Object Detection (FSOD) tries to solve detection with only limited annotated data. It relies on extensive pre-training and elaborated fine-tuning strategy and model design. Most FSOD approaches are designed and evaluated only on natural images which result in consistent performance drop when applied to other kind of images such as aerial ones. We propose, Few-Shot DiffusionDet (FSDD), a novel approach using a recent diffusion-based object detector. FSDD largely outperforms existing work on aerial images while remaining competitive with natural images. The versatility of FSDD is demonstrated with thorough experiments in the FSOD setting and with multiple kinds of images in the more challenging scenario of Cross-Domain FSOD.