  The recent advancements in generative language models have demonstrated their
ability to memorize knowledge from documents and recall knowledge to respond to
user queries effectively. Building upon this capability, we propose to enable
multimodal large language models (MLLMs) to memorize and recall images within
their parameters. Given a user query for visual content, the MLLM is
anticipated to "recall" the relevant image from its parameters as the response.
Achieving this target presents notable challenges, including inbuilt visual
memory and visual recall schemes within MLLMs. To address these challenges, we
introduce a generative cross-modal retrieval framework, which assigns unique
identifier strings to represent images and involves two training steps:
learning to memorize and learning to retrieve. The first step focuses on
training the MLLM to memorize the association between images and their
respective identifiers. The latter step teaches the MLLM to generate the
corresponding identifier of the target image, given the textual query input. By
memorizing images in MLLMs, we introduce a new paradigm to cross-modal
retrieval, distinct from previous discriminative approaches. The experiments
demonstrate that the generative paradigm performs effectively and efficiently
even with large-scale image candidate sets.
