人間とロボットのインタラクションにおける属性ベース物体参照のための合成的ゼロショット学習
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction
言語指示に含まれる属性の組み合わせから未知の物体をゼロショットで同定する手法を実装し、MIT-StatesとClothing 16Kで評価した。
著者: Peng Gao, Ahmed Jaafar, Brian Reily, Christopher Reardon, Hao Zhang
分類: cs.RO, cs.AI, cs.CL, cs.CV
原文アブストラクト
Language-enabled robots have been widely studied over the past years to enable natural human-robot interaction and teaming in various real-world applications. Language-enabled robots must be able to comprehend referring expressions to identify a particular object from visual perception using a set of referring attributes extracted from natural language. However, visual observations of an object may not be available when it is referred to, and the number of objects and attributes may also be unbounded in open worlds. To address the challenges, we implement an attribute-based compositional zero-shot learning method that uses a list of attributes to perform referring expression comprehension in open worlds. We evaluate the approach on two datasets including the MIT-States and the Clothing 16K. The preliminary experimental results show that our implemented approach allows a robot to correctly identify the objects referred to by human commands.
