  Knowledge retrieval with multi-modal queries plays a crucial role in
supporting knowledge-intensive multi-modal applications. However, existing
methods face challenges in terms of their effectiveness and training
efficiency, especially when it comes to training and integrating multiple
retrievers to handle multi-modal queries. In this paper, we propose an
innovative end-to-end generative framework for multi-modal knowledge retrieval.
Our framework takes advantage of the fact that large language models (LLMs) can
effectively serve as virtual knowledge bases, even when trained with limited
data. We retrieve knowledge via a two-step process: 1) generating knowledge
clues related to the queries, and 2) obtaining the relevant document by
searching databases using the knowledge clue. In particular, we first introduce
an object-aware prefix-tuning technique to guide multi-grained visual learning.
Then, we align multi-grained visual features into the textual feature space of
the LLM, employing the LLM to capture cross-modal interactions. Subsequently,
we construct instruction data with a unified format for model training.
Finally, we propose the knowledge-guided generation strategy to impose prior
constraints in the decoding steps, thereby promoting the generation of
distinctive knowledge clues. Through experiments conducted on three benchmarks,
we demonstrate significant improvements ranging from 3.0% to 14.6% across all
evaluation metrics when compared to strong baselines.
