PromptCap: Prompt-Guided Task-Aware Image Captioning (2211.09699v4)

Abstract: Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer. LLMs (LMs) like GPT-3 are particularly helpful for this task because of their strong knowledge retrieval and reasoning capabilities. To enable LM to understand images, prior work uses a captioning model to convert images into text. However, when summarizing an image in a single caption sentence, which visual entities to describe are often underspecified. Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. The prompt contains a question that the caption should aid in answering. To avoid extra annotation, PromptCap is trained by examples synthesized with GPT-3 and existing datasets. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60.4% on OK-VQA and 59.6% on A-OKVQA). Zero-shot results on WebQA show that PromptCap generalizes well to unseen domains.

PromptCap: Prompt-Guided Task-Aware Image Captioning for Enhanced VQA

The paper introduces "PromptCap," a model designed to enhance the task of knowledge-based Visual Question Answering (VQA) by improving image captioning processes. PromptCap serves as a bridge between images and black-box LLMs like GPT-3, addressing the challenge of ensuring images are summarized in a way that includes necessary details for accurate question answering.

Key Contributions and Methodology

PromptCap differs from generic captioning models by enabling the use of natural language prompts to control which visual entities are described. This allows the model to focus on aspects of the image pertinent to the question at hand, thereby enhancing the ability of LLMs like GPT-3 to derive relevant answers.

To avoid the need for additional annotated data, the authors employ GPT-3 to synthesize training examples from existing VQA datasets. These examples are filtered to ensure quality by using a test generation approach that checks if the given synthesized captions can facilitate correct answers to questions when used with the LLM.

Experimental Results

The paper demonstrates the efficacy of the PromptCap approach through its integration into a VQA pipeline. The model significantly outperforms existing generic captioning models, achieving state-of-the-art results on knowledge-based VQA tasks such as OK-VQA and A-OKVQA, with accuracies of 60.4% and 59.6%, respectively. The experiments also included zero-shot tasks on datasets like WebQA, showing generalizability beyond the specifically synthesized training data.

Implications and Future Directions

The work on PromptCap highlights the potential for improved vision-language interfacing by using customizable prompts to direct LLMs to focus on pertinent visual information. This approach could be expanded to include more diversified vision-language tasks beyond VQA, potentially broadening the scope of applications for prompt-guided captioning. Furthermore, integrating PromptCap in multi-modal AI systems could enhance interpretation capabilities where specific image details are crucial for decision-making or generating detailed narrative understandings.

The implications for large-scale applications in AI are significant, with the technology ready to improve human-AI interactions where visual context comprehension is required. Future research could explore the combination of PromptCap with models that allow for end-to-end fine-tuning, potentially unlocking even greater gains across various vision-based AI tasks.