Supervised Knowledge Retrieval and Reasoning for Effective Visual Question Answering

핵심 개념

Supervised retrieval of relevant knowledge from external knowledge bases and scene graphs, combined with multi-hop reasoning, can significantly improve performance on knowledge-based visual question answering tasks.

초록

The paper proposes a framework for knowledge-based visual question answering (KB-VQA) that focuses on effectively retrieving and integrating relevant knowledge from external knowledge bases and scene graphs.
The key highlights are:

The authors design a supervised retrieval model based on contrastive loss to retrieve relevant knowledge triplets from the external knowledge base and scene graph, conditioned on the given question.

The retrieved knowledge is integrated into both task-specific neural architectures and large language model (LLM) backbones to perform multi-hop reasoning and answer the questions.

The results show that the proposed supervised retrieval approach significantly improves the performance of both task-specific and LLM-based models compared to previous methods.

The analysis reveals that while LLMs are stronger in 1-hop reasoning, the task-specific model outperforms LLMs in 2-hop reasoning, even when provided with the relevant knowledge from both modalities. This highlights the importance of a strong reasoning module to effectively integrate and reason over the retrieved knowledge.

The authors also find that LLM models outperform the task-specific model for KB-related questions, confirming the effectiveness of the implicit knowledge in LLMs. However, LLMs do not fully alleviate the need for external knowledge bases.

Overall, the paper demonstrates the positive impact of supervised knowledge retrieval and effective integration of visual and external knowledge for improving performance on knowledge-based visual question answering tasks.

통계

The KRVQA dataset contains 32,910 images and 157,201 question-answer pairs, with supporting reasons indicating the source of knowledge (visual or external).
The external knowledge base consists of 225,434 factual triplets extracted from DBpedia, ConceptNet, and WebChild.

인용구

"Our results demonstrate the positive impact of empowering task-specific and LLM models with supervised external and visual knowledge retrieval models."
"Our findings show that though LLMs are stronger in 1-hop reasoning, they suffer in 2-hop reasoning in comparison with our fine-tuned NN model even if the relevant information from both modalities is available to the model."
"Moreover, we observed that LLM models outperform the NN model for KB-related questions which confirms the effectiveness of implicit knowledge in LLMs however, they do not alleviate the need for external KB."

핵심 통찰 요약

Find The Gap: Knowledge Base Reasoning For Visual Question Answering

by Elham J. Bar... 게시일 arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10226.pdf

더 깊은 질문

How can the proposed supervised retrieval approach be extended to handle dynamic knowledge retrieval during the reasoning process?

The proposed supervised retrieval approach can be extended to handle dynamic knowledge retrieval during the reasoning process by implementing a mechanism that continuously refines the retrieved knowledge based on the ongoing reasoning steps. This dynamic retrieval process can involve updating the retrieved knowledge based on the intermediate results of the reasoning process. Here are some key steps to extend the approach:

Incremental Retrieval: Instead of retrieving all relevant knowledge at once, the system can retrieve a subset of knowledge initially and then dynamically fetch additional information as the reasoning progresses. This incremental retrieval can be based on the relevance of the retrieved knowledge to the current state of reasoning.

Feedback Loop: Implement a feedback loop mechanism where the reasoning module provides feedback on the relevance and accuracy of the retrieved knowledge. This feedback can be used to adjust the retrieval process and fetch more relevant information in subsequent steps.

Adaptive Retrieval: Develop an adaptive retrieval strategy that adjusts the retrieval criteria based on the evolving context of the reasoning process. This can involve re-ranking the retrieved knowledge based on its importance in the current reasoning context.

Memory Mechanism: Introduce a memory mechanism that stores and updates relevant knowledge retrieved during the reasoning process. This memory can be accessed and modified dynamically to incorporate new information as the reasoning unfolds.

Real-time Retrieval: Implement a real-time retrieval system that continuously monitors the reasoning process and fetches relevant knowledge on-demand. This approach ensures that the system always has access to the most up-to-date information for accurate reasoning.

By incorporating these dynamic knowledge retrieval mechanisms, the supervised retrieval approach can adapt to the changing requirements of the reasoning process and enhance the model's ability to integrate external knowledge effectively.

How can the potential limitations of relying on implicit knowledge in LLMs be addressed to further improve knowledge-based visual question answering?

Relying on implicit knowledge in Large Language Models (LLMs) for knowledge-based visual question answering comes with certain limitations that need to be addressed to improve the overall performance of the system. Here are some strategies to mitigate these limitations:

Fine-tuning for Specific Tasks: Fine-tune the LLMs on specific knowledge-based visual question answering tasks to enhance their understanding of the domain-specific information. This fine-tuning process can help the models better capture relevant knowledge for accurate reasoning.

Hybrid Approaches: Combine implicit knowledge from LLMs with explicit knowledge retrieval mechanisms to complement each other's strengths. By integrating both implicit and explicit knowledge sources, the model can leverage the benefits of both approaches for improved performance.

Error Analysis and Correction: Conduct thorough error analysis to identify common pitfalls and inaccuracies in the implicit knowledge stored in LLMs. Develop mechanisms to correct these errors and refine the stored knowledge for more reliable reasoning.

Multi-hop Reasoning: Enhance the LLMs' capability for multi-hop reasoning to handle complex questions that require reasoning over multiple pieces of information. This can help overcome the limitations of LLMs in handling intricate knowledge-based queries.

Domain-specific Knowledge Injection: Inject domain-specific knowledge into the LLMs to supplement their implicit knowledge with targeted information relevant to the visual question answering task. This domain-specific injection can improve the model's understanding of the task requirements.

Regular Updates and Maintenance: Continuously update and maintain the implicit knowledge stored in LLMs to ensure its accuracy and relevance. Regularly refreshing the knowledge base can prevent outdated or incorrect information from affecting the reasoning process.

By implementing these strategies, the limitations of relying on implicit knowledge in LLMs can be mitigated, leading to more robust and accurate knowledge-based visual question answering systems.

Given the importance of high-level visual representations, how can the scene graph generation process be improved to provide more accurate and reliable visual knowledge for the KB-VQA task?

Improving the scene graph generation process is crucial for providing high-level visual representations that can enhance the accuracy and reliability of visual knowledge for Knowledge-Based Visual Question Answering (KB-VQA) tasks. Here are some approaches to enhance the scene graph generation process:

Object Detection Refinement: Enhance object detection algorithms to improve the accuracy of identifying objects in the scene. Utilize state-of-the-art object detection models and fine-tune them on visual question answering datasets to better capture relevant objects in the image.

Relationship Extraction: Develop advanced techniques for relationship extraction between objects in the scene. This involves identifying not just individual objects but also the interactions and connections between them to create a more detailed scene graph.

Semantic Segmentation Integration: Integrate semantic segmentation into the scene graph generation process to provide pixel-level understanding of objects in the image. This fine-grained segmentation can offer more precise object boundaries and relationships.

Contextual Information Inclusion: Incorporate contextual information into the scene graph generation process to capture the spatial and contextual relationships between objects. This contextual understanding can improve the richness of the scene graph representation.

Multi-Modal Fusion: Implement multi-modal fusion techniques to combine visual information with textual cues or external knowledge. By fusing different modalities of information, the scene graph can capture a more comprehensive understanding of the scene.

Quality Assessment Mechanisms: Develop quality assessment mechanisms to evaluate the accuracy and completeness of the generated scene graphs. Implement feedback loops to refine the scene graph generation process based on the quality assessment results.

End-to-End Training: Explore end-to-end training approaches that jointly optimize object detection, relationship extraction, and scene graph generation. This holistic training can improve the coherence and consistency of the scene graph representation.

By implementing these strategies, the scene graph generation process can be enhanced to provide more accurate and reliable visual knowledge for Knowledge-Based Visual Question Answering tasks, ultimately improving the performance and interpretability of the KB-VQA system.