Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning

• URL: http://arxiv.org/abs/2501.08597v1
• Date: Wed, 15 Jan 2025 05:45:04 GMT
• Title: Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning
• Authors: Julian Perry, Surasakdi Siripong, Thanakorn Phonchai,
• Abstract summary: We propose an Adaptive Knowledge-Guided Pretraining for Large Vision-Language Models (AKGP-LVLM) It incorporates structured and unstructured knowledge into LVLMs during pretraining and fine-tuning. We evaluate our method on four benchmark datasets, demonstrating significant performance improvements over state-of-the-art models.
• Score: 0.0
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• Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multimodal tasks, but their performance is often constrained by the lack of external knowledge integration, limiting their ability to handle knowledge-intensive tasks such as visual question answering and reasoning. To address this challenge, we propose a novel method, Adaptive Knowledge-Guided Pretraining for Large Vision-Language Models (AKGP-LVLM), which dynamically incorporates structured and unstructured knowledge into LVLMs during pretraining and fine-tuning. Our approach employs a knowledge encoder to represent external knowledge, a retrieval mechanism to select task-relevant information, and a dynamic adaptor to align multimodal and knowledge representations effectively. We evaluate our method on four benchmark datasets, demonstrating significant performance improvements over state-of-the-art models. Furthermore, human evaluations highlight the superior correctness and relevance of our model's outputs. Extensive analyses confirm the robustness, efficiency, and scalability of AKGP-LVLM, making it a compelling solution for real-world knowledge-intensive tasks.

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