SEMI-SUPERVISED ITERATIVE KEYPOINT AND VIEWPOINT INVARIANT FEATURE LEARNING FOR VISUAL RECOGNITION

摘要

A system and method for semi-supervised learning of visual recognition networks includes generating an initial set of feature representation training data based on simulated 2D test images of various viewpoints with respect to a target 3D rendering. A feature representation network generates feature representation vectors based on processing of the initial feature representation training data. Keypoint patches are labeled according to a score value based on a series of reference patches of unique viewpoint poses and a test keypoint patch processed through the trained feature representation network. A keypoint detector network learns keypoint detection based on processing of the keypoint detector training data. Output of the keypoint detector network learning is used as refined training data for successive iterations of the feature representation network learning, and output of successive iterations of the feature representation network learning is used as refined training data for the keypoint detector learning until convergence.