Selective State Space Memory for Large Vision-Language Models

• URL: http://arxiv.org/abs/2412.09875v1
• Date: Fri, 13 Dec 2024 05:40:50 GMT
• Title: Selective State Space Memory for Large Vision-Language Models
• Authors: Chee Ng, Yuen Fung,
• Abstract summary: State Space Memory Integration (SSMI) is a novel approach for efficient fine-tuning of LVLMs. SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively. experiments on benchmark datasets, including COCO Captioning, VQA, and Flickr30k, demonstrate that SSMI achieves state-of-the-art performance.
• Score: 0.0
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• Abstract: Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for efficient fine-tuning of LVLMs. By integrating lightweight Mamba-based state space modules into the LVLM architecture, SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively. Unlike traditional fine-tuning methods, SSMI requires only a fraction of the model's parameters to be updated, making it computationally efficient and scalable. Experiments on benchmark datasets, including COCO Captioning, VQA, and Flickr30k, demonstrate that SSMI achieves state-of-the-art performance while maintaining robustness and generalization capabilities. Comprehensive analysis further validates the advantages of SSMI in terms of efficiency, adaptability, and interpretability, positioning it as a compelling solution for fine-tuning large-scale vision-language models.

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