Libra: Building Decoupled Vision System on Large Language Models

• URL: http://arxiv.org/abs/2405.10140v1
• Date: Thu, 16 May 2024 14:34:44 GMT
• Title: Libra: Building Decoupled Vision System on Large Language Models
• Authors: Yifan Xu, Xiaoshan Yang, Yaguang Song, Changsheng Xu,
• Abstract summary: We introduce Libra, a prototype model with a decoupled vision system on a large language model (LLM) The decoupled vision system decouples inner-modal modeling and cross-modal interaction, yielding unique visual information modeling and effective cross-modal comprehension.
• Score: 63.28088885230901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce Libra, a prototype model with a decoupled vision system on a large language model (LLM). The decoupled vision system decouples inner-modal modeling and cross-modal interaction, yielding unique visual information modeling and effective cross-modal comprehension. Libra is trained through discrete auto-regressive modeling on both vision and language inputs. Specifically, we incorporate a routed visual expert with a cross-modal bridge module into a pretrained LLM to route the vision and language flows during attention computing to enable different attention patterns in inner-modal modeling and cross-modal interaction scenarios. Experimental results demonstrate that the dedicated design of Libra achieves a strong MLLM baseline that rivals existing works in the image-to-text scenario with merely 50 million training data, providing a new perspective for future multimodal foundation models. Code is available at https://github.com/YifanXu74/Libra.

Related papers

• Croc: Pretraining Large Multimodal Models with Cross-Modal Comprehension [21.500920290909843]
  We propose a new pretraining paradigm for Large Language Models (LLMs) to enhance their visual comprehension capabilities. Specifically, we design a dynamically learnable prompt token pool and employ the Hungarian algorithm to replace part of the original visual tokens with the most relevant prompt tokens. We present a new foundation model called Croc, which achieves new state-of-the-art performance on massive vision-language benchmarks.
  arXiv  Detail & Related papers  (2024-10-18T09:44:25Z)
• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
  We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
  We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary. We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
  arXiv  Detail & Related papers  (2024-03-12T14:58:52Z)
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks [92.03764152132315]
  We design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks. It has powerful visual capabilities and can be a good alternative to the ViT-22B.
  arXiv  Detail & Related papers  (2023-12-21T18:59:31Z)
• Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants [65.47222691674074]
  Muffin framework employs pre-trained vision-language models to act as providers of visual signals. UniMM-Chat dataset explores the complementarities of datasets to generate 1.1M high-quality and diverse multimodal instructions.
  arXiv  Detail & Related papers  (2023-10-01T12:35:18Z)
• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality [95.76661165594884]
  mPLUG-Owl is a training paradigm that equips large language models (LLMs) with multi-modal abilities. The training paradigm involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM. Experimental results show that our model outperforms existing multi-modal models.
  arXiv  Detail & Related papers  (2023-04-27T13:27:01Z)

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.