论文标题
视觉知识追踪
Visual Knowledge Tracing
论文作者
论文摘要
每年，成千上万的人都会学习新的视觉分类任务 - 放射科医生学习识别肿瘤，观鸟者学会区分相似的物种，并且人群工人学习如何为自主驾驶等应用程序注释有价值的数据。随着人类的了解，他们的大脑会更新其提取和关注的视觉功能，最终为他们的最终分类决策提供了信息。在这项工作中，我们提出了一项新的任务，即追踪人类学习者从事挑战性视觉分类任务的不断发展的分类行为。我们提出的模型可以共同提取学习者使用的视觉特征，并预测其使用的分类功能。我们从真正的人类学习者那里收集了三个挑战性的新数据集，以评估不同的视觉知识追踪方法的性能。我们的结果表明，我们的经常性模型能够预测人类学习者在三个具有挑战性的医学形象和物种识别任务上的分类行为。
Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers learn how to annotate valuable data for applications like autonomous driving. As humans learn, their brain updates the visual features it extracts and attend to, which ultimately informs their final classification decisions. In this work, we propose a novel task of tracing the evolving classification behavior of human learners as they engage in challenging visual classification tasks. We propose models that jointly extract the visual features used by learners as well as predicting the classification functions they utilize. We collect three challenging new datasets from real human learners in order to evaluate the performance of different visual knowledge tracing methods. Our results show that our recurrent models are able to predict the classification behavior of human learners on three challenging medical image and species identification tasks.
