MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering (2203.09138v1)

Abstract: Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding. One limitation of existing solutions is that they capture relevant knowledge from text-only knowledge bases, which merely contain facts expressed by first-order predicates or language descriptions while lacking complex but indispensable multimodal knowledge for visual understanding. How to construct vision-relevant and explainable multimodal knowledge for the VQA scenario has been less studied. In this paper, we propose MuKEA to represent multimodal knowledge by an explicit triplet to correlate visual objects and fact answers with implicit relations. To bridge the heterogeneous gap, we propose three objective losses to learn the triplet representations from complementary views: embedding structure, topological relation and semantic space. By adopting a pre-training and fine-tuning learning strategy, both basic and domain-specific multimodal knowledge are progressively accumulated for answer prediction. We outperform the state-of-the-art by 3.35% and 6.08% respectively on two challenging knowledge-required datasets: OK-VQA and KRVQA. Experimental results prove the complementary benefits of the multimodal knowledge with existing knowledge bases and the advantages of our end-to-end framework over the existing pipeline methods. The code is available at https://github.com/AndersonStra/MuKEA.

• The paper introduces a novel triplet-based framework to incorporate multimodal knowledge, significantly boosting Visual Question Answering performance.
• It leverages pre-training and fine-tuning with three loss functions for structural, consistency, and semantic alignment.
• It achieves a 3.35% and 6.08% improvement on OK-VQA and KRVQA, reducing errors compared to traditional retrieval models.

Overview of "MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering"

The research paper titled "MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering" presents an advanced technique for incorporating multimodal knowledge into Visual Question Answering (VQA) systems. This paper confronts the challenge of integrating external knowledge for enhancing the depth of cross-modal scene understanding in VQA tasks. Unlike traditional approaches that rely heavily on text-only knowledge bases, MuKEA introduces a sophisticated method for capturing and representing multimodal knowledge—a pivotal requirement for understanding and answering complex visual questions.

Methodology

MuKEA emphasizes the construction and utilization of a multimodal knowledge base using triplet representations. Each triplet consists of three components: a head entity representing visual objects, a relation embedding capturing the implicit relationship, and a tail entity denoting fact answers. This triplet representation stands as a novel approach in the VQA domain, designed to seamlessly integrate multimodal information.

To generate these representations, MuKEA employs a pre-training and fine-tuning strategy that iteratively accumulates both generic and domain-specific multimodal knowledge. This process is driven by three loss functions: the Triplet TransE Loss for structural alignment of triplet components, the Triplet Consistency Loss for maintaining topological consistency, and the Semantic Consistency Loss for mapping entities into a unified semantic space.

Results

MuKEA demonstrates substantial improvements over state-of-the-art methods in knowledge-based VQA tasks, achieving a notable 3.35% and 6.08% improvement on the OK-VQA and KRVQA datasets, respectively. This performance underscores the complementary benefits that MuKEA's multimodal knowledge provides beyond existing knowledge bases. Moreover, MuKEA’s end-to-end framework eliminates reliability on traditional 'retrieve-and-read' techniques, mitigating cascading errors common in complex reasoning tasks.

Implications and Future Directions

The implications of MuKEA are significant for both practical applications and theoretical advancements in VQA systems. Its ability to model higher-order relationships and provide explainable reasoning aligns well with the demands for transparency and accuracy in AI applications. The technique's adaptability for integrating diverse types of knowledge makes it feasible for various VQA applications where multimodal reasoning is necessary.

Looking forward, the development of MuKEA hints at new research directions including the fusion of MuKEA with existing structured knowledge graphs, potentially maximizing the strengths of both approaches. Additionally, examining the transferability of MuKEA's accumulative knowledge strategy across various datasets and VQA scenarios could further elucidate its utility and scalability in real-world applications.

In conclusion, MuKEA represents an important step forward in VQA research, providing a robust framework that leverages multimodal knowledge in a novel triplet format to enhance the interpretability and efficacy of VQA systems. As such, it sets a foundation for future innovations in multimodal understanding and visual cognition.