Reasoning About Object Affordances in a Knowledge Base Representation
Abstract
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.
Cite
Text
Zhu et al. "Reasoning About Object Affordances in a Knowledge Base Representation." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10605-2_27Markdown
[Zhu et al. "Reasoning About Object Affordances in a Knowledge Base Representation." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/zhu2014eccv-reasoning/) doi:10.1007/978-3-319-10605-2_27BibTeX
@inproceedings{zhu2014eccv-reasoning,
title = {{Reasoning About Object Affordances in a Knowledge Base Representation}},
author = {Zhu, Yuke and Fathi, Alireza and Fei-Fei, Li},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {408-424},
doi = {10.1007/978-3-319-10605-2_27},
url = {https://mlanthology.org/eccv/2014/zhu2014eccv-reasoning/}
}