SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding
- URL: http://arxiv.org/abs/2412.09604v1
- Date: Thu, 12 Dec 2024 18:59:26 GMT
- Title: SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding
- Authors: Hao Li, Changyao Tian, Jie Shao, Xizhou Zhu, Zhaokai Wang, Jinguo Zhu, Wenhan Dou, Xiaogang Wang, Hongsheng Li, Lewei Lu, Jifeng Dai,
- Abstract summary: We propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation.
We introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding.
Our code and models shall be released.
- Score: 66.74446220401296
- License:
- Abstract: The remarkable success of Large Language Models (LLMs) has extended to the multimodal domain, achieving outstanding performance in image understanding and generation. Recent efforts to develop unified Multimodal Large Language Models (MLLMs) that integrate these capabilities have shown promising results. However, existing approaches often involve complex designs in model architecture or training pipeline, increasing the difficulty of model training and scaling. In this paper, we propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation. To address challenges identified in existing encoder-free unified MLLMs, we introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding while reducing training complexity. After being trained on large-scale mixed image-text data with a unified next-token prediction objective, SynerGen-VL achieves or surpasses the performance of existing encoder-free unified MLLMs with comparable or smaller parameter sizes, and narrows the gap with task-specific state-of-the-art models, highlighting a promising path toward future unified MLLMs. Our code and models shall be released.
Related papers
- ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance [47.53085562765585]
We introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model.
To address the large dataset size typically required for image-text alignment, we propose to enhance data efficiency through the design of a vision tokenizer.
To promote synergistic enhancement between understanding and generation capabilities, which is under-explored in previous works, we introduce a novel self-enhancing multimodal alignment scheme.
arXiv Detail & Related papers (2024-12-09T17:11:50Z) - Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges [15.850548556536538]
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language.
An advanced subset of these models, known as Multimodal Large Language Models (MLLMs), extends LLM capabilities to process and interpret multiple data modalities.
This survey provides a comprehensive overview of the recent advancements in LLMs.
arXiv Detail & Related papers (2024-12-04T11:14:06Z) - MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding [6.538592344967826]
We introduce MUSE-VL, a Unified Vision-Language Model Semantic through discrete -language for multimodal understanding and generation.
The proposed model significantly surpasses the previous state-of-the-art in various vision-language benchmarks and achieves better performance than dedicated understanding models.
arXiv Detail & Related papers (2024-11-26T03:33:52Z) - SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs [40.74693126923826]
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities.
Training adapters with image-level supervision often results in significant misalignment.
We introduce Supervised Embedding Alignment (SEA), a token-level alignment method that leverages vision-language pre-trained models.
arXiv Detail & Related papers (2024-08-21T17:58:02Z) - CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models [68.64605538559312]
In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.
Inspired by our findings, we propose a measurement to quantitatively evaluate the learning balance.
In addition, we introduce an auxiliary loss regularization method to promote updating of the generation distribution of MLLMs.
arXiv Detail & Related papers (2024-07-29T23:18:55Z) - SOLO: A Single Transformer for Scalable Vision-Language Modeling [74.05173379908703]
We present SOLO, a single transformer for visiOn-Language mOdeling.
A unified single Transformer architecture, like SOLO, effectively addresses these scalability concerns in LVLMs.
In this paper, we introduce the first open-source training recipe for developing SOLO, an open-source 7B LVLM.
arXiv Detail & Related papers (2024-07-08T22:40:15Z) - Making LLaMA SEE and Draw with SEED Tokenizer [69.1083058794092]
We introduce SEED, an elaborate image tokenizer that empowers Large Language Models with the ability to SEE and Draw.
With SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe.
SEED-LLaMA has exhibited compositional emergent abilities such as multi-turn in-context multimodal generation.
arXiv Detail & Related papers (2023-10-02T14:03:02Z) - mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal
Skip-connections [104.14624185375897]
mPLUG is a new vision-language foundation model for both cross-modal understanding and generation.
It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering.
arXiv Detail & Related papers (2022-05-24T11:52:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.