Exploring State Space Models for Multimodal Learning with VL-Mamba

Conceitos essenciais

VL-Mamba explores state space models for multimodal learning, offering a promising alternative to Transformer-based architectures.

Resumo

Abstract:

Proposes VL-Mamba based on state space models for multimodal learning.
Empirically explores vision selective scan mechanisms and different model combinations.

Introduction:

Discusses the significance of multimodal large language models (MLLM).

Related Work:

Details the evolution of state space models (SSMs) and their variants.

Method:

Introduces the architecture of VL-Mamba with Vision Encoder, MultiModal Connector, and Mamba LLM.

Experiment:

Conducts experiments on various benchmarks showcasing competitive performance.

Quantitative Evaluation:

Compares VL-Mamba with other SoTA models on multiple benchmarks.

Qualitative Result:

Provides examples of responses generated by VL-Mamba.

Ablation Study:

Evaluates different variants of language models, vision encoders, MMC architectures, and scan mechanisms.

Limitation:

Focuses on applying the 2D selective scan without exploring training data's impact.

Estatísticas

The computational burden increases quadratically with sequence length due to self-attention mechanism in Transformers.

Citações

"VL-Mamba explores state space models for multimodal learning tasks."
"Our model achieves competitive performance with other small MLLMs and even outperforms large MLLMs on some benchmarks."

Principais Insights Extraídos De

VL-Mamba

by Yanyuan Qiao... às arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13600.pdf

Perguntas Mais Profundas

How can higher-quality training data improve the benchmark performance

Higher-quality training data can significantly improve benchmark performance by providing the model with more diverse and representative examples to learn from. With high-quality data, the model can better generalize to unseen scenarios, leading to improved accuracy and robustness. Additionally, quality data helps in mitigating biases and noise that may be present in lower-quality datasets, resulting in more reliable predictions. Moreover, a larger and more varied dataset allows the model to capture complex patterns and relationships effectively, enhancing its overall performance on different tasks.

What are the implications of using SigLIP-SO as the vision encoder in VL-Mamba

Using SigLIP-SO as the vision encoder in VL-Mamba has several implications for the model's performance. SigLIP-SO is known for its ability to encode visual information efficiently and effectively, making it a suitable choice for processing image inputs in multimodal tasks. By leveraging SigLIP-SO as the vision encoder, VL-Mamba benefits from advanced visual representation capabilities that enhance its understanding of images and their context within multimodal learning scenarios. This leads to improved alignment between visual and textual modalities, ultimately enhancing the model's overall performance on various benchmarks.

How might incorporating additional modalities beyond vision and language enhance the capabilities of state space models

Incorporating additional modalities beyond vision and language into state space models can expand their capabilities in various ways. By integrating modalities such as audio or sensor data, state space models can gain a deeper understanding of complex real-world environments by capturing multi-sensory information. This integration enables models to perform tasks requiring cross-modal reasoning or fusion of information from different sources more effectively.
Furthermore, incorporating additional modalities enhances the versatility of state space models across a wider range of applications such as robotics (for sensor fusion), healthcare (for analyzing medical imaging alongside patient records), or autonomous driving (integrating visual inputs with LiDAR data). The inclusion of multiple modalities provides richer context for decision-making processes within these domains, leading to more comprehensive solutions powered by state-of-the-art modeling techniques like those based on state space models.