Efficient Meta-Episodic Learning with Dynamic Task Sampling for Improved CLIP-based Point Cloud Classification

A novel meta-episodic learning framework with dynamic task sampling is proposed to effectively encode unknown generalized class information into CLIP-based point cloud classification models, enabling improved performance on challenging and underrepresented classes.