Keypoint Based

Keypoint-based methods are increasingly used to analyze images and videos, focusing on identifying and tracking salient points to understand object pose, motion, and relationships. Current research emphasizes efficient algorithms, such as graph convolutional networks and implicit keypoint representations, to improve accuracy and speed, particularly in challenging scenarios like low-resolution images or long-range tracking in real-world videos. These advancements have significant implications for applications ranging from robotics and augmented reality (e.g., 6D pose estimation, portrait animation) to forensic science (e.g., copy-move forgery detection). The development of large-scale annotated datasets is also crucial for training robust and generalizable keypoint-based models.