Beyond Object Recognition: A New Benchmark towards Object Concept Learning

作者：Yong–Lu Li, Xu Yue, Xinyu Xu, Xiaohan Mao, Yuan Yao, Siqi Liu, Cewu Lu · 年份：2023 · DOI：10.1109/iccv51070.2023.01833 · 被引用次数：6 · 研究领域：Multimodal Machine Learning Applications、Domain Adaptation and Few-Shot Learning、Machine Learning and Data Classification

Understanding objects is a central building block of AI, especially for embodied AI. Even though object recognition excels with deep learning, current machines struggle to learn higher-level knowledge, e.g., what attributes an object has, and what we can do with it. Here, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out affordances and simultaneously give the reason: what attributes make an object possess these affordances. To support OCL, we build a densely annotated knowledge base including extensive annotations for three levels of object concept (category, attribute, affordance), and the clear causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages concept instantiation and causal intervention to infer the three levels. 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.