利用深度提高现实环境中的指称表达理解
Using Depth for Improving Referring Expression Comprehension in Real-World Environments
摘要：
在人机协作任务中，机器人通过寻找所描述的对象来帮助其伙伴，深度维度在成功完成任务中起着关键作用。现有的研究大多集中在理解使用RGB图像的对象描述。然而，包括深度信息的三维空间感知在现实世界环境中是基本的。在这项工作中，我们提出了一种方法来识别所描述的对象，考虑深度维度数据。在深度数据对消除对象歧义至关重要的场景中，以及在我们的整个评估数据集中，使用深度特征可以显着提高性能，其中包含可以指定和不指定深度维度的对象。
英文摘要：
In a human-robot collaborative task where a robot helps its partner by finding described objects, the depth dimension plays a critical role in successful task completion. Existing studies have mostly focused on comprehending the object descriptions using RGB images. However, 3-dimensional space perception that includes depth information is fundamental in real-world environments. In this work, we propose a method to identify the described objects considering depth dimension data. Using depth features significantly improves performance in scenes where depth data is critical to disambiguate the objects and across our whole evaluation dataset that contains objects that can be specified with and without the depth dimension.
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。
