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VL-Mamba: Exploring State Space Models for Multimodal Learning

Yanyuan Qiao, Zheng Yu, Longteng Guo, Sihan Chen, Zijia Zhao, Mingzhen Sun, Qi Wu, Jing Liu

TL;DR

<3-5 sentence high-level summary> Problem: Transformer-based multimodal large language models incur quadratic attention costs for long sequences. Approach: VL-Mamba substitutes the Transformer backbone with the state-space model-driven Mamba LLM and introduces a 2D Vision Selective Scan-based Multimodal Connector to fuse vision and language efficiently. Contributions: first application of SSMs to multimodal learning, designs include Bidirectional and Cross 2D scan mechanisms, multiple MMC variants, and extensive ablations and benchmarks showing competitive results with smaller backbones. Significance: demonstrates scalable, long-range multimodal reasoning with SSMs and provides open-source avenues for further exploration.

Abstract

Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity and results in expensive computational overhead. Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space models, which have been shown to have great potential for long-sequence modeling with fast inference and linear scaling in sequence length. Specifically, we first replace the transformer-based backbone language model such as LLama or Vicuna with the pre-trained Mamba language model. Then, we empirically explore how to effectively apply the 2D vision selective scan mechanism for multimodal learning and the combinations of different vision encoders and variants of pretrained Mamba language models. The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.

VL-Mamba: Exploring State Space Models for Multimodal Learning

TL;DR

<3-5 sentence high-level summary> Problem: Transformer-based multimodal large language models incur quadratic attention costs for long sequences. Approach: VL-Mamba substitutes the Transformer backbone with the state-space model-driven Mamba LLM and introduces a 2D Vision Selective Scan-based Multimodal Connector to fuse vision and language efficiently. Contributions: first application of SSMs to multimodal learning, designs include Bidirectional and Cross 2D scan mechanisms, multiple MMC variants, and extensive ablations and benchmarks showing competitive results with smaller backbones. Significance: demonstrates scalable, long-range multimodal reasoning with SSMs and provides open-source avenues for further exploration.

Abstract

Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity and results in expensive computational overhead. Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space models, which have been shown to have great potential for long-sequence modeling with fast inference and linear scaling in sequence length. Specifically, we first replace the transformer-based backbone language model such as LLama or Vicuna with the pre-trained Mamba language model. Then, we empirically explore how to effectively apply the 2D vision selective scan mechanism for multimodal learning and the combinations of different vision encoders and variants of pretrained Mamba language models. The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.
Paper Structure (24 sections, 7 equations, 4 figures, 5 tables)

This paper contains 24 sections, 7 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. State Space Models (SSMs)
  4. Multimodal Large Language Model (MLLM)
  5. Method
  6. Preliminaries
  7. VL-Mamba Model
  8. Overall Architecture
  9. Vision Encoder
  10. MultiModal Connector (MMC)
  11. Mamba LLM
  12. Experiment
  13. Experimental Setup
  14. Implementation details
  15. MLLM Benchmarks
  16. ...and 9 more sections

Figures (4)

  • Figure 1: The architecture of VL-Mamba. It contains a Vision Encoder, a MultiModal Connector (MMC), and a language model. We utilize the pre-trained Mamba Large Language Model (Mamba LLM) as its language model, and the pre-trained Vision Transformer model as its vision encoder.
  • Figure 2: Three architectures of MultiModal Connector: (a) MLP; (b) MLP-VSS; (c) VSS-2 Linear Layer.
  • Figure 3: Illustration of two different Vision Selective Scan (VSS) Mechanisms: Bidirectional-Scan Mechanism (BSM) (top) and Cross-Scan Mechanism (CSM) (bottom).
  • Figure 4: Examples of response generated by VL-Mamba.