mlsanityData to 7 Oct 2026

GLAMI-1M: A Multilingual Image-Text Fashion Dataset

arXiv 2211.14451 ↗First posted 17 Nov 2022cs.CV cs.CL cs.LGcited by 3

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Abstract (arXiv)

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. The dataset, source code and model checkpoints are published at https://github.com/glami/glami-1m

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 17 Nov 2022.

Builds on

45 of 72 references.

References · 45

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33 more
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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Modelscited by 20
  2. 2024mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpuscited by 8
  3. 2025FLUID: Flow-Latent Unified Integration via Token Distillation for Expert Specialization in Multimodal Learningcited by 1