Does Object Recognition Work for Everyone?

Terrance de Vries, Ishan Misra, Changhan Wang, Laurens van der Maaten; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 52-59

Abstract


The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels.

Related Material


[pdf]
[bibtex]
@InProceedings{Vries_2019_CVPR_Workshops,
author = {de Vries, Terrance and Misra, Ishan and Wang, Changhan and van der Maaten, Laurens},
title = {Does Object Recognition Work for Everyone?},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}