# Beyond Text: Frozen Large Language Models in Visual Signal Comprehension

arXiv: https://arxiv.org/abs/2403.07874 · HTML: https://mlsanity.com/paper/2403.07874/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 61 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 30 of 55)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)

## Built on by (most cited first, 23 of 23)

- [Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%](https://mlsanity.com/paper/2406.11837/) (2024)
- [Autoregressive Models in Vision: A Survey](https://mlsanity.com/paper/2411.05902/) (2024)
- [Language-Guided Image Tokenization for Generation](https://mlsanity.com/paper/2412.05796/) (2024)
- [TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation](https://mlsanity.com/paper/2406.10450/) (2024)
- [FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training](https://mlsanity.com/paper/2411.11927/) (2024)
- [SweetTok: Semantic-Aware Spatial-Temporal Tokenizer for Compact Video Discretization](https://mlsanity.com/paper/2412.10443/) (2024)
- [MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization](https://mlsanity.com/paper/2504.00999/) (2025)
- [Factorized Visual Tokenization and Generation](https://mlsanity.com/paper/2411.16681/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [LG-VQ: Language-Guided Codebook Learning](https://mlsanity.com/paper/2405.14206/) (2024)
- [V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer](https://mlsanity.com/paper/2501.04975/) (2025)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [Multi-View Empowered Structural Graph Wordification for Language Models](https://mlsanity.com/paper/2406.15504/) (2024)
- [Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation](https://mlsanity.com/paper/2506.18658/) (2025)
- [V2Flow: Unifying Visual Tokenization and Large Language Model Vocabularies for Autoregressive Image Generation](https://mlsanity.com/paper/2503.07493/) (2025)
- [Omni-AutoThink: Adaptive Multimodal Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.03783/) (2025)
- [Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech](https://mlsanity.com/paper/2412.11409/) (2024)
- [Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text](https://mlsanity.com/paper/2503.01261/) (2025)
- [Quantizing Text-attributed Graphs for Semantic-Structural Integration](https://mlsanity.com/paper/2507.19526/) (2025)
- [Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques](https://mlsanity.com/paper/2506.04788/) (2025)
- [Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model](https://mlsanity.com/paper/2510.02403/) (2025)
- [SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs](https://mlsanity.com/paper/2602.22716/) (2026)
- [Routing-Based Continual Learning for Multimodal Large Language Models](https://mlsanity.com/paper/2511.01831/) (2025)

## Abstract (arXiv)

In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The foundational concept of our method views an image as a linguistic entity, and translates it to a set of discrete words derived from the LLM's vocabulary. To achieve this, we present the Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, which transforms an image into a ``foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model. With this innovative image encoding, the LLM gains the ability not only for visual comprehension but also for image denoising and restoration in an auto-regressive fashion-crucially, without any fine-tuning. We undertake rigorous experiments to validate our method, encompassing understanding tasks like image recognition, image captioning, and visual question answering, as well as image denoising tasks like inpainting, outpainting, deblurring, and shift restoration. Code and models are available at https://github.com/zh460045050/V2L-Tokenizer.