In this paper, the LCVO modular method is proposed for the Visual Question Answering (VQA) Grounding task in the vision-language multimodal domain. This approach relies on a frozen large language model (LLM) as intermediate mediator between the off-the-shelf VQA model and the off-the-shelf Open-Vocabulary Object Detection (OVD) model, where the LLM transforms and conveys textual information between the two modules based on a designed prompt. LCVO establish an integrated plug-and-play framework without the need for any pre-training process. This framework can be deployed for VQA Grounding tasks under low computational resources. The modularized model within the framework allows application with various state-of-the-art pre-trained models, exhibiting significant potential to be advance with the times. Experimental implementations were conducted under constrained computational and memory resources, evaluating the proposed method's performance on benchmark datasets including GQA, CLEVR, and VizWiz-VQA-Grounding. Comparative analyses with baseline methods demonstrate the robust competitiveness of LCVO.

翻译：本文提出了一种名为LCVO的模块化方法，用于解决视觉-语言多模态领域的视觉问答（VQA）定位任务。该方法将冻结的大语言模型（LLM）作为中间媒介，连接现成的VQA模型与现成的开放词汇目标检测（OVD）模型——其中LLM通过设计的提示词，在两个模块之间转换并传递文本信息。LCVO构建了一个无需任何预训练过程的集成式即插即用框架，可在低计算资源条件下部署于VQA定位任务。该框架内的模块化模型支持与多种先进预训练模型协同应用，展现出显著的时效性演进潜力。在受限计算和内存资源环境下进行的实验实施中，本方法在GQA、CLEVR和VizWiz-VQA-Grounding等基准数据集上的性能得到评估。与基线方法的对比分析表明，LCVO具有稳健的竞争力。