Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations

• URL: http://arxiv.org/abs/2306.08658v2
• Date: Wed, 12 Jun 2024 09:33:29 GMT
• Title: Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
• Authors: Gregor Geigle, Radu Timofte, Goran Glavaš,
• Abstract summary: We introduce Babel-ImageNet, a massively multilingual benchmark that offers partial translations of ImageNet labels to 100 languages. We evaluate 11 public multilingual CLIP models on our benchmark, demonstrating a significant gap between English ImageNet performance and that of high-resource languages. We show that the performance of multilingual CLIP can be drastically improved for low-resource languages with parameter-efficient language-specific training.
• Score: 53.89380284760555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They are, however, mostly evaluated in English as multilingual benchmarks are limited in availability. We introduce Babel-ImageNet, a massively multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. We instead automatically obtain reliable translations by linking them -- via shared WordNet synsets -- to BabelNet, a massively multilingual lexico-semantic network. We evaluate 11 public multilingual CLIP models on zero-shot image classification (ZS-IC) on our benchmark, demonstrating a significant gap between English ImageNet performance and that of high-resource languages (e.g., German or Chinese), and an even bigger gap for low-resource languages (e.g., Sinhala or Lao). Crucially, we show that the models' ZS-IC performance highly correlates with their performance in image-text retrieval, validating the use of Babel-ImageNet to evaluate multilingual models for the vast majority of languages without gold image-text data. Finally, we show that the performance of multilingual CLIP can be drastically improved for low-resource languages with parameter-efficient language-specific training. We make our code and data publicly available: \url{https://github.com/gregor-ge/Babel-ImageNet}

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