  In this paper, the LCV2 modular method is proposed for the Grounded Visual
Question Answering 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 visual grounding (VG) model,
where the LLM transforms and conveys textual information between the two
modules based on a designed prompt. LCV2 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 LCV2.
