论文标题
视觉概念 - 环孔科的学习
Visual Concept-Metaconcept Learning
论文作者
论文摘要
人类理由带有概念和元观念：我们从视觉输入中认识到红色和绿色；我们还了解它们描述了对象的相同属性（即颜色）。在本文中，我们提出了视觉概念 - 曲局概论学习者（VCML），用于从图像以及相关的问题 - 答案对的概念和掌声的联合学习。关键是要利用视觉概念和元评估之间的双向连接。视觉表示为预测看不见的概念之间的关系提供了基础线索。知道红色和绿色描述了对象的相同属性，我们概括了以下事实：立方体和球体也描述了对象的相同属性，因为它们都对对象的形状进行了分类。同时，有关元观察的知识使视觉概念从有限，嘈杂甚至有偏见的数据中学习。从仅几个紫色立方体的例子中，我们就可以理解一种新的紫色，它类似于立方体的色调，而不是它们的形状。对合成和现实世界数据集的评估验证了我们的主张。
Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associated question-answer pairs. The key is to exploit the bidirectional connection between visual concepts and metaconcepts. Visual representations provide grounding cues for predicting relations between unseen pairs of concepts. Knowing that red and green describe the same property of objects, we generalize to the fact that cube and sphere also describe the same property of objects, since they both categorize the shape of objects. Meanwhile, knowledge about metaconcepts empowers visual concept learning from limited, noisy, and even biased data. From just a few examples of purple cubes we can understand a new color purple, which resembles the hue of the cubes instead of the shape of them. Evaluation on both synthetic and real-world datasets validates our claims.
