Scene Centric

Scene-centric approaches in computer vision focus on understanding entire scenes rather than individual objects, aiming to learn robust representations that capture complex relationships between scene elements. Current research emphasizes developing efficient models, such as transformers and those incorporating online clustering or contrastive learning, to handle the computational challenges posed by the high dimensionality of scene data. These advancements are improving performance in tasks like 3D pose estimation, motion prediction, and semantic segmentation, with applications ranging from autonomous driving to assistive robotics. The ability to effectively process and understand complex scenes is crucial for building more intelligent and adaptable systems.