论文标题
走向体现的场景描述
Towards Embodied Scene Description
论文作者
论文摘要
实施例是所有智能代理（生物和机器人）的重要特征，而现有场景说明任务主要集中于被动分析图像，对场景的语义理解与代理与环境之间的相互作用分开。在这项工作中，我们提出了体现的场景描述，该描述利用了代理在其环境中找到最佳视点的实施方案，以进行场景描述任务。建立了具有模仿学习和强化学习范式的学习框架，以教导智能代理人生成相应的感觉运动活动。在AI2THOR数据集和现实世界的机器人平台上都测试了所提出的框架，以证明已开发方法的有效性和可扩展性。
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment. In this work, we propose the Embodied Scene Description, which exploits the embodiment ability of the agent to find an optimal viewpoint in its environment for scene description tasks. A learning framework with the paradigms of imitation learning and reinforcement learning is established to teach the intelligent agent to generate corresponding sensorimotor activities. The proposed framework is tested on both the AI2Thor dataset and a real world robotic platform demonstrating the effectiveness and extendability of the developed method.
