Salient Keypoints for Interactive Meta-Learning (SKIML)

摘要: Learning to recognize new objects in real time in unconstrained environments presents significant challenges for robotic platforms. We present a meta-learning solution to this problem as well as a registered image and events dataset to facilitate work in this domain. Our solution uses interactive motion to isolate the object, and motion-based saliency (from events) to select relevant keypoints from a high-resolution RGB image. Salient keypoints are then passed to a meta-learner to classify the object type. We show that using our interactive isolation and keypoint selection approach, we outperform existing techniques by 6-20%.