论文标题
超越对象识别：针对对象概念学习的新基准测试
Beyond Object Recognition: A New Benchmark towards Object Concept Learning
论文作者
论文摘要
理解对象是人工智能的中央组成部分，尤其是对于体现的AI而言。即使对象识别在深度学习方面表现出色，但当前的机器仍在努力学习更高级别的知识，例如，对象具有什么属性，以及我们可以用对象做什么。在这项工作中，我们提出了一个具有挑战性的对象概念学习（OCL）任务，以推动对象理解的信封。它需要机器来推理对象负担，并同时给出原因：什么属性使对象具有这些负担。为了支持OCL，我们建立了一个密集注释的知识库，其中包括三个级别的对象概念（类别，属性，负担能力）的广泛标签和三个层次的因果关系。通过分析OCL的因果结构，我们提出了一个基线，对象概念推理网络（OCRN）。它利用因果关系和概念实例化来推断其因果关系后的三个层次。在实验中，OCRN在遵循因果关系的同时有效地注入对象知识。我们的数据和代码可在https://mvig-rhos.com/ocl上找到。
Understanding objects is a central building block of artificial intelligence, especially for embodied AI. Even though object recognition excels with deep learning, current machines still struggle to learn higher-level knowledge, e.g., what attributes an object has, and what can we do with an object. In this work, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out object affordances and simultaneously give the reason: what attributes make an object possesses these affordances. To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages causal intervention and concept instantiation to infer the three levels following their causal relations. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.
