Zero Shot Instance Segmentation

Zero-shot instance segmentation aims to identify and delineate objects in images or 3D scenes without requiring any training data for the specific object categories present. Current research focuses on leveraging powerful pre-trained models, such as Segment Anything Model (SAM), to transfer knowledge to smaller, more efficient models for tasks like panoramic semantic segmentation and 3D instance segmentation. This approach addresses the limitations of traditional methods that rely on extensive labeled datasets, enabling broader applicability across diverse object categories and environments. The resulting advancements have significant implications for robotics, autonomous systems, and other fields requiring efficient and adaptable object recognition.