利用可解释性理解现实世界中的指称表达
Leveraging Explainability for Comprehending Referring Expressions in the Real World
摘要：
对于有效的人机协作，机器人理解用户的请求并在存在歧义时提出合理的后续问题至关重要。在理解请求中的用户的对象描述的同时，现有的研究集中于可以用现有的目标检测和定位模块检测或定位的有限对象类别的挑战。另一方面，在野外，不可能限制在交互期间可能遇到的对象类别。为了理解描述的对象和解决在野外的歧义，第一次，我们提出了一种利用可解释性的方法。我们的方法专注于一个场景的活动区域，找到所描述的对象，而不把以前的对象类别和自然语言指令的限制。我们在各种真实世界的图像中评估我们的方法，并观察到我们的方法所建议的区域可以帮助解决歧义。当我们将我们的方法与最先进的基线进行比较时，我们表明我们的方法在具有模糊对象的场景中表现更好，这些对象无法通过现有的对象检测器识别。
英文摘要：
For effective human-robot collaboration, it is crucial for robots to understand requests from users and ask reasonable follow-up questions when there are ambiguities. While comprehending the users' object descriptions in the requests, existing studies have focused on this challenge for limited object categories that can be detected or localized with existing object detection and localization modules. On the other hand, in the wild, it is impossible to limit the object categories that can be encountered during the interaction. To understand described objects and resolve ambiguities in the wild, for the first time, we suggest a method by leveraging explainability. Our method focuses on the active regions of a scene to find the described objects without putting the previous constraints on object categories and natural language instructions. We evaluate our method in varied real-world images and observe that the regions suggested by our method can help resolve ambiguities. When we compare our method with a state-of-the-art baseline, we show that our method performs better in scenes with ambiguous objects which cannot be recognized by existing object detectors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
