New Approach to Knowledge-Based Visual Question Answering

Visual Question Answering (VQA) is a task that combines images and questions to give accurate answers. Some questions need extra information that isn't found in the image itself. This is where knowledge-based Visual Question Answering (K-VQA) comes in. K-VQA needs both an image and additional knowledge from outside sources to provide the correct answer.

In the past, K-VQA methods often relied on external databases to find information, and they used supervised learning to train their models. However, newer approaches have started using large language models (LLMs) that are pre-trained and can answer questions without needing much extra training. While these methods are effective, they often do not make clear where the necessary knowledge comes from or how they reach their answers, which can be a drawback.

This article focuses on a new approach to K-VQA that harnesses the capabilities of LLMs to generate knowledge statements that can be used to answer questions in a zero-shot manner, meaning without any prior examples or training for that specific task.

#The Traditional Approach to K-VQA

Traditional K-VQA methods typically operate in a few steps. First, they gather relevant knowledge from external sources like Wikipedia or other databases. Then, they train a model using labeled data made up of pairs of images, questions, and answers. This method works but has its challenges. It needs a lot of labeled data and a suitable external knowledge source, which may not always be available in real-world scenarios.

#Recent Advances with Language Models

With recent improvements in LLMs, researchers have begun applying these models for K-VQA tasks. These LLMs contain a vast amount of knowledge from different sources. Existing methods often start by turning an image into descriptive text, known as captions, and then combine these captions with the questions to ask the LLM for answers.

However, a major limitation of these methods is that they don't explicitly state what knowledge was used to reach an answer. This lack of transparency can lead to issues, especially when the right external information is crucial for answering the questions.

#The Need for Explainability

To address these limitations, there's a growing interest in making K-VQA systems more interpretable. When users know how a system makes decisions, it builds trust. In K-VQA, having explicit knowledge statements can not only boost performance but also help users understand how the system arrives at its answers.

#The New Approach: Knowledge Generation

This new method focuses on generating knowledge from LLMs to answer questions effectively. Here's how it works:

1. Generating Knowledge: The system produces relevant knowledge statements using an LLM. This knowledge relates directly to the image and question pairs.

2. Diversity of Knowledge: To enhance the output, the method includes a strategy for generating multiple diverse pieces of knowledge statements. This helps cover different aspects of the same question, increasing the chances of providing the correct answer.

3. Combining Knowledge with Questions: The generated knowledge statements, along with the Image Captions, are passed to the LLM to get the final answer.

#Evaluating the New Method

To validate the effectiveness of this new approach, two datasets often used in K-VQA tasks were employed: OK-VQA and A-OKVQA. These datasets require external knowledge to answer questions and have specific guidelines for testing performance.

#Results of the New Method

Experiments show that the new knowledge-generation approach significantly improves Answer Accuracy. The generated knowledge proved to be relevant and helpful in many cases, outperforming several existing methods that do not utilize this additional knowledge.

#Comparison with Traditional Methods

In comparison to traditional methods, where external knowledge was fetched from knowledge bases, the newly proposed method reduces the need for extensive training data. It remains effective even without previous examples, using only the image and the question.

#The Knowledge Generation Process

The knowledge generation entails two main steps:

1. Initial Generation: For each image-question pair, one knowledge statement is generated using a well-crafted prompt. The prompt guides the LLM to create a relevant piece of knowledge.

2. Diversification: The generated knowledge undergoes a diversification process to produce multiple statements. This is achieved by selecting diverse demonstrations to encourage varied outputs from the LLM.

#Caption Generation

A crucial part of the knowledge generation process is converting images into text descriptions. Captions serve as the context that LLMs need to generate relevant knowledge. A question-aware captioning approach is used, which focuses on significant parts of the image that relate to the question being asked.

#The Role of Prompts in Knowledge Generation

Prompts are essential for guiding the LLMs in generating relevant knowledge statements. The prompts include clear instructions and contextual information to help the model understand what is being asked.

#Integration of Generated Knowledge in K-VQA

Once the relevant knowledge statements are generated, they are combined with the image captions and the question. This complete package is then processed by the LLM to produce the answer. Different pre-trained models can be used for this process, each affecting the overall performance differently.

#Evaluation Metrics

To assess the effectiveness of the knowledge generation method, various metrics are utilized:

• Grammaticality: Checks if the knowledge statements are written correctly.
• Relevance: Evaluates if the statements relate well to the questions and images.
• Fact-checking: Determines if the statements are factual.
• Helpfulness: Measures if the knowledge aids in reaching the correct answer.
• Diversity: Assesses the range of generated knowledge statements.

#Results and Findings

After rigorous testing, the results indicate that incorporating generated knowledge consistently leads to better performance in answering questions. It is essential to strike a balance between the quantity of knowledge generated, as too much can lead to redundancy or noise.

Human evaluations showed that while most generated knowledge was relevant and grammatical, there were instances where the knowledge could mislead or confuse. Therefore, continuous refinement of the knowledge generation process is necessary.

#Future Directions

To enhance the effectiveness of this approach, future work could focus on:

• Reducing Redundancy: Filtering out unnecessary knowledge that doesn't add value.
• Improving Image Descriptions: Using better image captioning techniques to ensure the LLM has enough context to generate relevant knowledge.
• Exploring New Models: Using advanced vision-language models that can process images alongside texts directly.

#Conclusion

In conclusion, generating knowledge from LLMs for K-VQA presents a viable solution to addressing the challenges faced by traditional methods. The experiments demonstrate significant improvements in performance, making this a promising direction for future research in visual question answering. By focusing on generating and combining relevant knowledge with image captions, the method not only enhances accuracy but also fosters explainability, ultimately benefiting users and practitioners in the field.