Rare Object Detection

Rare object detection focuses on identifying objects that appear infrequently within large datasets, a challenge across diverse fields like satellite imagery analysis, medical imaging, and autonomous driving. Current research emphasizes developing efficient data augmentation techniques, including methods that leverage existing data to synthesize new examples of rare objects and improve the efficiency of human annotation efforts. These advancements, often employing deep learning models such as diffusion models and transformer networks, are crucial for improving the accuracy and reliability of object detection systems in data-scarce scenarios, ultimately impacting applications ranging from environmental monitoring to medical diagnosis.