Object Prediction

Object prediction in computer vision aims to accurately locate and classify objects within images and videos, often extending to predicting their future states or relationships. Current research emphasizes improving prediction accuracy through novel loss functions (like Unified-IoU), advanced model architectures such as transformers (e.g., Relationformer) and incorporating contextual information (e.g., temporal context via historical object prediction, or textual descriptions). These advancements are crucial for applications ranging from robotic manipulation and autonomous driving to improved object detection in challenging scenarios like camouflage and open-vocabulary settings, ultimately leading to more robust and versatile computer vision systems.