Yang, S., Luo, S., & Han, S. C. (2024). Multimodal Commonsense Knowledge Distillation for Visual Question Answering. arXiv preprint arXiv:2411.02722v1.
This research paper aims to address the limitations of existing Visual Language Models (VLMs) in Visual Question Answering (VQA) tasks that require external commonsense knowledge. The authors propose a novel framework to improve VQA performance by effectively integrating commonsense knowledge with visual and textual information.
The proposed framework constructs a unified relational graph incorporating commonsense knowledge, visual objects from images, and question representations. This graph structure captures the relationships between these different modalities. A Graph Convolutional Network (GCN) is then employed to learn from this enriched graph, effectively encoding the multimodal information and commonsense knowledge. The trained GCN acts as a teacher model, distilling the learned knowledge to student models of varying sizes and architectures. This knowledge distillation process enhances the student models' ability to answer VQA questions requiring external commonsense reasoning.
The proposed framework demonstrates significant performance improvements on the ScienceQA dataset compared to baseline models of various sizes and complexities. Notably, even large, sophisticated VLMs benefit from the integration of commonsense knowledge through this framework. This highlights the effectiveness and robustness of the proposed approach in enhancing VQA capabilities.
The research concludes that integrating commonsense knowledge into VQA models significantly improves their performance, particularly for questions requiring reasoning beyond visual information. The proposed graph-based multimodal commonsense knowledge distillation framework provides a computationally efficient and flexible approach to achieve this integration, benefiting various VQA model architectures.
This research contributes to the field of VQA by addressing the crucial challenge of incorporating commonsense knowledge. The proposed framework offers a practical and effective solution, potentially impacting the development of more robust and intelligent VQA systems.
While the framework shows promising results, further exploration with different datasets and knowledge bases could provide a more comprehensive evaluation. Additionally, investigating the impact of different graph construction techniques and GCN architectures could lead to further performance improvements.
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