Part Based

Part-based approaches in computer vision and machine learning aim to decompose complex objects or tasks into smaller, more manageable components for improved understanding, robustness, and interpretability. Current research focuses on developing models that learn these part representations effectively, employing techniques like diffusion models for 3D object generation, non-negative training for reinforcement learning, and multi-scale architectures for robust image recognition. These advancements are driving progress in diverse applications, including 3D printing, robotics, explainable AI, and drug discovery, by enabling more accurate, efficient, and insightful analyses of data and systems.