Learning Based Perception

Learning-based perception focuses on using machine learning to enable computers to "see" and understand their environment, primarily for applications like autonomous driving. Current research emphasizes improving model robustness and efficiency by developing novel training strategies (e.g., incorporating realistic augmentations and unsupervised learning techniques) and refining evaluation metrics (e.g., focusing on safety-critical aspects like ego-centric object detection). This field is crucial for advancing the safety and reliability of autonomous systems, particularly by addressing limitations of current models in handling diverse and unpredictable real-world scenarios.