From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models

• URL: http://arxiv.org/abs/2212.10846v3
• Date: Mon, 8 May 2023 06:04:04 GMT
• Title: From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
• Authors: Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, Steven C.H. Hoi
• Abstract summary: Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. We propose emphImg2Prompt, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections.
• Score: 111.42052290293965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnection and task disconnection between LLM and VQA task. End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. To address this issue, we propose \emph{Img2Prompt}, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training. In order to provide such prompts, we further employ LLM-agnostic models to provide prompts that can describe image content and self-constructed question-answer pairs, which can effectively guide LLM to perform zero-shot VQA tasks. Img2Prompt offers the following benefits: 1) It can flexibly work with various LLMs to perform VQA. 2)~Without the needing of end-to-end training, it significantly reduces the cost of deploying LLM for zero-shot VQA tasks. 3) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo \cite{Deepmind:Flamingo2022} by 5.6\% on VQAv2. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20\%.

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