重新审视EmbodiedQA：一个简单的基线和超越
Revisiting EmbodiedQA: A Simple Baseline and Beyond
摘要：
在问答式问答中，代理与环境交互以收集回答用户问题所需的信息。现有的工作为解决这个有趣的问题奠定了坚实的基础。但目前的性能，特别是在导航方面，表明并行QA可能对当代方法太具挑战性。在本文中，我们对这个问题进行了实证研究，并介绍了1）一个简单而有效的基线，可以实现有希望的性能; 2）一个更容易和实用的设置，让代理有机会在实际回答用户问题之前，将训练好的模型适应新的环境。在这个新的设置中，我们随机将一些对象放置在新的环境中，并通过蒸馏网络升级代理策略，以保留训练模型的泛化能力。在标准设置下，我们的简单基线在QuarteredQA v1基准测试中实现了与最先进水平非常有竞争力的结果;在新的设置中，我们发现设置中引入的微小变化在导航中产生了显着的增益。
英文摘要：
In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be too challenging for the contemporary approaches. In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions. In this new setting, we randomly place a few objects in new environments, and upgrade the agent policy by a distillation network to retain the generalization ability from the trained model. On the EmbodiedQA v1 benchmark, under the standard setting, our simple baseline achieves very competitive results to the-state-of-the-art; in the new setting, we found the introduced small change in settings yields a notable gain in navigation.
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。
