面向机器人应用的语义图像搜索
Semantic Image Search for Robotic Applications
摘要：
泛化是机器人学中最重要的问题之一。新的综合方法使用互联网数据库来解决新的任务。现代搜索引擎可以在几毫秒内根据查询返回大量信息。然而，并非所有返回的信息都与任务相关，部分原因是多义词的问题。在这里，我们专门解决了使用图像搜索的对象泛化问题。我们提出了一个双模态的解决方案，结合视觉和文本信息，基于观察，人类使用额外的语言线索来划分预期的词义。我们通过将其与人类标记的数据进行比较来评估我们的方法的质量，并发现，平均而言，我们的方法与Google搜索相比会导致改进的结果，并且它可以处理多义词的问题。
英文摘要：
Generalization in robotics is one of the most important problems. New generalization approaches use internet databases in order to solve new tasks. Modern search engines can return a large amount of information according to a query within milliseconds. However, not all of the returned information is task relevant, partly due to the problem of polysemes. Here we specifically address the problem of object generalization by using image search. We suggest a bi-modal solution, combining visual and textual information, based on the observation that humans use additional linguistic cues to demarcate intended word meaning. We evaluate the quality of our approach by comparing it to human labelled data and find that, on average, our approach leads to improved results in comparison to Google searches, and that it can treat the problem of polysemes.
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