Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models (2024.emnlp-main)
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| Challenge: | PIXEL is a vision transformer that has been pre-trained on rendered text . however, it is not able to outperform monolingual subwords like BERT . |
| Approach: | They propose to use PIXEL as a vision transformer to train on rendered text to explore the gap between its visual and linguistic understanding. |
| Outcome: | The proposed model outperforms monolingual subword models in most other contexts, but it lacks the linguistic knowledge to perform in language tasks. |
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| Challenge: | pixel-based models can be used to transfer learning from standard languages to dialects . pretrained language models achieve strong results for languages seen during training, but their performance declines with out-of-domain dialects. |
| Approach: | They compare pixel-based models to token-based ones to evaluate dialects . standard german is tokenized in a more meaningful way, whereas the Bern dialect is tokenize in pixel form . |
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Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer (2023.emnlp-main)
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| Challenge: | Recent work shows that pixel representations can be finetuned across scripts without vocabulary extensions, adapters, or transliteration. |
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Multilingual Pretraining for Pixel Language Models (2025.emnlp-main)
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| Challenge: | PIXEL-M4 model pretrains on four visually and linguistically diverse languages . previous work on pixel-based language models focused on monolingual pretraining on English data . |
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Text Rendering Strategies for Pixel Language Models (2023.emnlp-main)
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| Approach: | They propose four approaches to rendering text in a PIXEL model using character bigrams and patch frequency biases. |
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| Challenge: | Recent studies show that deep vision-only and language-only models project inputs into a partially aligned representational space. |
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Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks (2023.findings-acl)
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| Challenge: | Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. |
| Approach: | They propose to use open-source, open-access language models to make visual input accessible to the model using separate verbalisation models. |
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From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation (2026.acl-long)
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| Challenge: | pixel-based language modeling integrates visual and textual data to improve performance of language models. |
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PIXAR: Auto-Regressive Language Modeling in Pixel Space (2024.findings-acl)
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| Challenge: | Recent work shows the possibility of building open-vocabulary large language models that operate on pixel representations. |
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Are Visual-Linguistic Models Commonsense Knowledge Bases? (2022.coling-1)
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| Challenge: | PTLMs are used to extract knowledge from text on demand. |
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