潜在埋め込みによるタスク適応としての少数ショットキーポイント検出
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
キーポイント埋め込みを条件として疎なネットワークを適応させ、密な汎用性と疎な精度を両立するキーポイント追跡手法を提案し、実機ピック・アンド・プレースでゼロショット転移を示した。
著者: Mel Vecerik, Jackie Kay, Raia Hadsell, Lourdes Agapito, Jon Scholz
分類: cs.RO, cs.CV
原文アブストラクト
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics. Existing approaches either compute dense keypoint embeddings in a single forward pass, meaning the model is trained to track everything at once, or allocate their full capacity to a sparse predefined set of points, trading generality for accuracy. In this paper we explore a middle ground based on the observation that the number of relevant points at a given time are typically relatively few, e.g. grasp points on a target object. Our main contribution is a novel architecture, inspired by few-shot task adaptation, which allows a sparse-style network to condition on a keypoint embedding that indicates which point to track. Our central finding is that this approach provides the generality of dense-embedding models, while offering accuracy significantly closer to sparse-keypoint approaches. We present results illustrating this capacity vs. accuracy trade-off, and demonstrate the ability to zero-shot transfer to new object instances (within-class) using a real-robot pick-and-place task.
