Perception Model

Perception models aim to enable machines to understand and interpret sensory information, mirroring human perception. Current research emphasizes improving model robustness and efficiency through techniques like data augmentation with physics-based simulations, uncertainty quantification for resource-efficient foundation model refinement, and the integration of multimodal data (e.g., visual and textual information) using architectures such as transformers and generative models. These advancements are crucial for enhancing the reliability and safety of applications like autonomous driving, robotics, and medical imaging, where accurate and robust perception is paramount.