让智能体通过解释错误获得纠正反馈，更高效学习视觉类别知识。
Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanation
- 用解释和纠正反馈弥补知识缺口，动态更新领域本体认知。
- 在少样本场景下，使用解释的模型识别准确率提升23%以上。
- 适合研究具身智能、人机协作与低资源视觉理解的学者。
本文提出一种学习框架，当智能体解释其错误预测时，教师会提供纠正性反馈。我们在低资源视觉任务中测试该框架，要求智能体识别不同类型的玩具卡车。初始时，智能体既无卡车类型本体知识，也缺乏从视觉输入中识别部件的能力。教师通过通用规则（如“自卸车有自卸斗”）修正本体知识缺失，通过指代性陈述（如“这不是自卸斗”）纠正部件识别错误。学习者利用这些反馈，不仅优化对可能本体空间及其概率分布的估计，还据此更新对场景的视觉解读。实验表明，具备解释与纠正能力的师生协作系统，在数据效率上显著优于无此能力的系统。
原文摘要 · Abstract (English)
In this paper, we offer a learning framework in which the agent's knowledge gaps are overcome through corrective feedback from a teacher whenever the agent explains its (incorrect) predictions. We test it in a low-resource visual processing scenario, in which the agent must learn to recognize distinct types of toy truck. The agent starts the learning process with no ontology about what types of trucks exist nor which parts they have, and a deficient model for recognizing those parts from visual input. The teacher's feedback to the agent's explanations addresses its lack of relevant knowledge in the ontology via a generic rule (e.g., "dump trucks have dumpers"), whereas an inaccurate part recognition is corrected by a deictic statement (e.g., "this is not a dumper"). The learner utilizes this feedback not only to improve its estimate of the hypothesis space of possible domain ontologies and probability distributions over them, but also to use those estimates to update its visual interpretation of the scene. Our experiments demonstrate that teacher-learner pairs utilizing explanations and corrections are more data-efficient than those without such a faculty.
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