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Turath-150K: Image Database of Arab Heritage | | Dani Kiyasseh
; Rasheed El-Bouri
; | | Date: |
Sat, 1 Jan 2022 17:36:25 GMT (18924kb,D) | | Abstract: | Large-scale image databases remain largely biased towards objects and
activities encountered in a select few cultures. This absence of
culturally-diverse images, which we refer to as the hidden tail, limits the
applicability of pre-trained neural networks and inadvertently excludes
researchers from under-represented regions. To begin remedying this issue, we
curate Turath-150K, a database of images of the Arab world that reflect
objects, activities, and scenarios commonly found there. In the process, we
introduce three benchmark databases, Turath Standard, Art, and UNESCO,
specialised subsets of the Turath dataset. After demonstrating the limitations
of existing networks pre-trained on ImageNet when deployed on such benchmarks,
we train and evaluate several networks on the task of image classification. As
a consequence of Turath, we hope to engage machine learning researchers in
under-represented regions, and to inspire the release of additional
culture-focused databases. The database can be accessed here:
danikiyasseh.github.io/Turath. | | Source: | arXiv, 2201.00220 | | Services: | Forum | Review | PDF | Favorites | |
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