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NLLB-CLIP -- train performant multilingual image retrieval model on a budget

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arxiv 2309.01859 v3 pith:KNP5RAXH submitted 2023-09-04 cs.CV

Alexander Visheratin This is my paper · ORCID
classification cs.CV
keywords modelmodelsimagenllb-clipbudgetcreateddatasetdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Today, the exponential rise of large models developed by academic and industrial institutions with the help of massive computing resources raises the question of whether someone without access to such resources can make a valuable scientific contribution. To explore this, we tried to solve the challenging task of multilingual image retrieval having a limited budget of $1,000. As a result, we present NLLB-CLIP - CLIP model with a text encoder from the NLLB model. To train the model, we used an automatically created dataset of 106,246 good-quality images with captions in 201 languages derived from the LAION COCO dataset. We trained multiple models using image and text encoders of various sizes and kept different parts of the model frozen during the training. We thoroughly analyzed the trained models using existing evaluation datasets and newly created XTD200 and Flickr30k-200 datasets. We show that NLLB-CLIP is comparable in quality to state-of-the-art models and significantly outperforms them on low-resource languages.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Meta CLIP 2: A Worldwide Scaling Recipe

    cs.CV 2025-07 conditional novelty 7.0 of 10

    A data curation and training recipe that scales CLIP from English-only data to 300+ languages from scratch, breaking the curse of multilinguality at ViT-H/14 scale.

  2. Scaling Vision-Language Models Is Not Enough to Mitigate Bias

    cs.CV 2026-07 accept novelty 6.5 of 10

    Model scale loses predictive power for multi-attribute bias robustness in VLMs, while training-data size and curation remain the more reliable levers.

  3. FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Frozen LLM text encoders, combined with multi-prompt hidden-state extraction and cached embeddings, make CLIP-style pre-training data-efficient, long-context aware, and multilingual.

  4. jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images

    cs.CL 2024-12 conditional novelty 5.0 of 10

    jina-clip-v2, an 865M-parameter multilingual dual-encoder, outperforms prior CLIP-style models on text-only and crossmodal retrieval, and on visually rich document retrieval, while supporting flexible embedding dimensions.

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