Remember, Retrieve and Generate: Understanding Infinite Visual Concepts
  as Your Personalized Assistant
Authors: name: Haoran Hao, name: Jiaming Han, name: Changsheng Li, name: Yu-Feng Li, name: Xiangyu Yue

The development of large language models (LLMs) has significantly enhanced
the capabilities of multimodal LLMs (MLLMs) as general assistants. However,
lack of user-specific knowledge still restricts their application in human's
daily life. In this paper, we introduce the Retrieval Augmented Personalization
(RAP) framework for MLLMs' personalization. Starting from a general MLLM, we
turn it into a personalized assistant in three steps. (a) Remember: We design a
key-value database to store user-related information, e.g., user's name, avatar
and other attributes. (b) Retrieve: When the user initiates a conversation, RAP
will retrieve relevant information from the database using a multimodal
retriever. (c) Generate: The input query and retrieved concepts' information
are fed into MLLMs to generate personalized, knowledge-augmented responses.
Unlike previous methods, RAP allows real-time concept editing via updating the
external database. To further improve generation quality and alignment with
user-specific information, we design a pipeline for data collection and create
a specialized dataset for personalized training of MLLMs. Based on the dataset,
we train a series of MLLMs as personalized multimodal assistants. By
pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual
concepts without additional finetuning. Our models demonstrate outstanding
flexibility and generation quality across a variety of tasks, such as
personalized image captioning, question answering and visual recognition. The
code, data and models are available at https://github.com/Hoar012/RAP-MLLM.