Learning Object Attributes with Category-Free Grounded Language from Deep Featurization
Learning Object Attributes with Category-Free Grounded Language from Deep Featurization
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DOI：
10.1109/iros45743.2020.9340824
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发表时间：
2020-10
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影响因子：
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通讯作者：
Luke E. Richards;Kasra Darvish;Cynthia Matuszek
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文献类型：
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作者：
Luke E. Richards;Kasra Darvish;Cynthia Matuszek
虽然扎根语言学习，即学习相对于机器人操作的物理世界的语言意义，是人-机器人交互研究的一个主要领域，但大多数研究都是在封闭世界或领域受限的环境中进行的。通过将基于CNN的视觉特征与自然语言标签相结合，我们提出了一种语言基于视觉感知而不使用范畴约束的系统。我们展示了可与针对特定特征使用手工制作的特征所取得的结果相媲美的结果，这是将语言基础转移到完全开放的世界识别空间的一步。
While grounded language learning, or learning the meaning of language with respect to the physical world in which a robot operates, is a major area in human-robot interaction studies, most research occurs in closed worlds or domain-constrained settings. We present a system in which language is grounded in visual percepts without using categorical constraints by combining CNN-based visual featurization with natural language labels. We demonstrate results comparable to those achieved using handcrafted features for specific traits, a step towards moving language grounding into the space of fully open world recognition.