Reasoning about objects and their affordances is a fundamental problem for visual intelligence. Most of the previous work casts this problem as a classification task where separate classifiers are trained to label objects, recognize attributes, or assign affordances. In this work, we consider the problem of object affordance reasoning using a knowledge base representation. Diverse information of objects are first harvested from images and other meta-data sources. We then learn a knowledge base (KB) using a Markov Logic Network (MLN). Given the learned KB, we show that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zero-shot affordance prediction and object recognition given human poses. © 2014 Springer International Publishing.
CITATION STYLE
Zhu, Y., Fathi, A., & Fei-Fei, L. (2014). Reasoning about object affordances in a knowledge base representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8690 LNCS, pp. 408–424). Springer Verlag. https://doi.org/10.1007/978-3-319-10605-2_27
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