GLAMI-1M: A Multilingual Image-Text Fashion Dataset
Vaclav Kosar (GLAMI), Antonín Hoskovec (GLAMI), Milan Šulc (Rossum.ai),* Radek Bartyzal (GLAMI)
The 33rd British Machine Vision Conference
Abstract
We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text.Video
Citation
@inproceedings{Kosar_2022_BMVC,
author = {Vaclav Kosar and Antonín Hoskovec and Milan Šulc and Radek Bartyzal},
title = {GLAMI-1M: A Multilingual Image-Text Fashion Dataset},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0607.pdf}
}
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