LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering

Understand

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.