# Contrastive Language-Image Pre-training for the Italian Language

arXiv: https://arxiv.org/abs/2108.08688 · HTML: https://mlsanity.com/paper/2108.08688/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://mlsanity.com/paper/2012.15613/) (2020)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients](https://mlsanity.com/paper/2010.07468/) (2020)
- [What the [MASK]? Making Sense of Language-Specific BERT Models](https://mlsanity.com/paper/2003.02912/) (2020)
- [BERT Goes Shopping: Comparing Distributional Models for Product Representations](https://mlsanity.com/paper/2012.09807/) (2020)

## Built on by (most cited first, 19 of 19)

- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)
- [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://mlsanity.com/paper/2211.06679/) (2022)
- [Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages](https://mlsanity.com/paper/2308.12038/) (2023)
- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [NLLB-CLIP -- train performant multilingual image retrieval model on a budget](https://mlsanity.com/paper/2309.01859/) (2023)
- [UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification](https://mlsanity.com/paper/2312.11038/) (2023)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [Open-Vocabulary Object Detection using Pseudo Caption Labels](https://mlsanity.com/paper/2303.13040/) (2023)
- [Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations](https://mlsanity.com/paper/2306.08658/) (2023)
- [CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages](https://mlsanity.com/paper/2310.13683/) (2023)
- [Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability](https://mlsanity.com/paper/2401.07087/) (2024)
- [C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap](https://mlsanity.com/paper/2309.03921/) (2023)
- [UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation](https://mlsanity.com/paper/2306.14067/) (2023)
- [Bridging Text and Image for Artist Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2410.09566/) (2024)
- [Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning](https://mlsanity.com/paper/2505.13628/) (2025)
- [SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages](https://mlsanity.com/paper/2609.30739/) (2026)

## Abstract (arXiv)

CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not trivial, since data in other languages might be not enough and the model needs high-quality translations of the texts to guarantee a good performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms the multilingual CLIP model on the tasks of image retrieval and zero-shot classification.