Class Agnostic Object

Class-agnostic object detection and related tasks aim to identify and analyze objects in images or 3D scenes without relying on pre-defined object categories. Current research focuses on developing robust methods using neural radiance fields (NeRFs), vision-language models (VLMs), and event cameras, often incorporating techniques like prompt engineering, contrastive learning, and diffusion models to improve object discovery and pose estimation. These advancements are significant for applications such as autonomous driving, robotics, and 3D scene understanding, enabling more flexible and adaptable systems capable of handling unseen or novel objects.