Główne pojęcia
VL-Mamba explores state space models for multimodal learning, offering a promising alternative to Transformer-based architectures.
Streszczenie
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.
Statystyki
The computational burden increases quadratically with sequence length due to self-attention mechanism in Transformers.
Cytaty
"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."