Rare Object

Rare object detection and manipulation pose significant challenges across various fields, from robotics to computer vision. Current research focuses on improving the performance of machine learning models, particularly vision-language models (VLMs) and 3D object detectors, in identifying and interacting with these infrequently occurring objects, often employing techniques like data augmentation and novel benchmark datasets to address class imbalances and improve model robustness. These advancements are crucial for enhancing the capabilities of autonomous systems, improving the efficiency of search and retrieval systems, and advancing our understanding of how artificial intelligence handles data scarcity.