Attribute-Centric Recognition for Cross-Category Generalization
Abstract
We propose an approach to find and describe objects within broad domains. We introduce a new dataset that provides annotation for sharing models of appearance and correlation across categories. We use it to learn part and category detectors. These serve as the visual basis for an integrated model of objects. We describe objects by the spatial arrangement of their attributes and the interactions between them. Using this model, our system can find animals and vehicles that it has not seen and infer attributes, such as function and pose. Our experiments demonstrate that we can more reliably locate and describe both familiar and unfamiliar objects, compared to a baseline that relies purely on basic category detectors.
Cite
Text
Farhadi et al. "Attribute-Centric Recognition for Cross-Category Generalization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539924Markdown
[Farhadi et al. "Attribute-Centric Recognition for Cross-Category Generalization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/farhadi2010cvpr-attribute/) doi:10.1109/CVPR.2010.5539924BibTeX
@inproceedings{farhadi2010cvpr-attribute,
title = {{Attribute-Centric Recognition for Cross-Category Generalization}},
author = {Farhadi, Ali and Endres, Ian and Hoiem, Derek},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {2352-2359},
doi = {10.1109/CVPR.2010.5539924},
url = {https://mlanthology.org/cvpr/2010/farhadi2010cvpr-attribute/}
}