KAT: A Knowledge Augmented Transformer for Vision-and-Language (2112.08614v2)

Abstract: The primary focus of recent work with largescale transformers has been on optimizing the amount of information packed into the model's parameters. In this work, we ask a different question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explored approaches under the paradigm of knowledge retrieval followed by answer prediction, but leave open questions about the quality and relevance of the retrieved knowledge used, and how the reasoning processes over implicit and explicit knowledge should be integrated. To address these challenges, we propose a novel model - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result (+6 points absolute) on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an end to end encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. An additional benefit of explicit knowledge integration is seen in improved interpretability of model predictions in our analysis.

• The paper presents a dual-method approach that extracts implicit knowledge via GPT-3 prompts and explicit knowledge using a contrastive CLIP-based method.
• It employs an encoder-decoder architecture that fuses both knowledge streams, achieving state-of-the-art performance on the OK-VQA benchmark.
• The integration of explicit knowledge boosts interpretability and efficacy, paving the way for advanced AI systems in complex vision-and-language tasks.

Overview of "KAT: A Knowledge Augmented Transformer for Vision-and-Language"

The paper introduces KAT (Knowledge Augmented Transformer), an innovative model designed to address the complex problem of integrating implicit and explicit knowledge within vision-and-language tasks. The central premise revolves around advancing multimodal transformers to not only encode information within their parameters but also leverage external explicit knowledge sources, thereby enhancing reasoning capabilities.

Core Contributions

The paper delineates several key contributions:

1. Knowledge Extraction Techniques: The authors propose a dual-method approach for extracting knowledge. Implicit knowledge is sourced from a frozen GPT-3 model through novel prompts, while explicit knowledge is retrieved using a contrastive-learning approach via the CLIP model. This method anchors the retrieved knowledge to visually-aligned entities, improving relevance and reducing noise.
2. Encoder-Decoder Architecture for Reasoning: A distinguishing feature of KAT is its novel reasoning module embedded within an encoder-decoder transformer framework. This module facilitates joint reasoning over both implicit and explicit knowledge streams during answer generation, ensuring holistic integration.
3. Empirical Results and Benchmark Performance: KAT demonstrates superior results on OK-VQA, a challenging open-domain multimodal task, achieving state-of-the-art performance. This position is bolstered by a significant performance improvement over previous models utilizing similar datasets.

Technical Insights

KAT presents a structured approach to the retrieval and reasoning over knowledge sources. By harnessing the latent commonsense stored in large-scale LLMs (implicit knowledge) and discrete entries from structured knowledge bases (explicit knowledge), the model navigates the complexity inherent in vision-and-language tasks more effectively than previous methods. The dual-retriever architecture of KAT exemplifies sophisticated knowledge alignment strategies that resolve ambiguities common in previous unimodal retrieval methods.

Importantly, the integration of explicit knowledge substantially boosts interpretability without sacrificing performance, contrasting favorably with existing models that primarily rely on implicit strategies.

Implications and Future Directions

The implications of this research are manifold. Practically, the enhanced reasoning capabilities of KAT could be leveraged across various domains requiring robust AI interaction, such as autonomous systems and complex query handling. Theoretically, the approach offers a foundation for future exploration into more sophisticated multimodal reasoning mechanisms, potentially fostering developments in areas such as human-computer interaction and cognitive AI systems.

Further investigations could focus on optimizing the balance between implicit and explicit knowledge utilization and exploring additional external knowledge sources for richer model training. Additionally, extending the research into real-world applications could validate the model's utility beyond controlled datasets, highlighting its adaptability and scalability in diverse contexts.

In summary, the paper provides a comprehensive examination of the integrative role of explicit knowledge in multimodal transformers, setting the stage for future explorations in AI reasoning and decision-making processes.