Class Agnostic Motion Prediction

Class-agnostic motion prediction focuses on forecasting the movement of objects in a scene without requiring prior knowledge of their specific class labels. Current research emphasizes developing robust models that generalize well to unseen scenarios and data distributions, often employing hybrid approaches combining deep learning with rule-based methods or leveraging self-supervised learning techniques to reduce reliance on expensive labeled data. These advancements are crucial for applications like autonomous driving, where reliable prediction of diverse object movements is paramount for safe and efficient navigation. Significant progress is being made using various architectures, including transformers and those incorporating spatial and temporal consistency regularizations to improve prediction accuracy and robustness.