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This repo is the official implementation of "Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning" accepted by AAAI 2025.

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# RAGPT

This repo is the official implementation of _Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning_ accepted by AAAI 2025.

Paper Link: https://arxiv.org/pdf/2501.01120

> [!IMPORTANT]
>
> *We release the **first comprehensive collection of resources on modality-missing learning**: https://github.com/Jian-Lang/awesome-modality-missing-learning.*

## News and Updates

+ 2026.5.1 🔥 Our work, AOEPT, which breaks the Implicit Modality-Reduction Bottleneck in Modality-Missing Prompt Tuning with a minimalist design, is accepted by [ICML 2026](https://arxiv.org/abs/2605.24816). Code is in https://github.com/Jian-Lang/AOEPT.
+ 2026.4.14 🔨 We release the first comprehensive collection of resources on modality-missing learning: https://github.com/Jian-Lang/awesome-modality-missing-learning.
+ 2025.8.12 🔨 We release the source code for Prompt-based baselines in this repo, including MAPs and MSPs, for better follow.
+ 2025.5.15 🔥 The extension of RAGPT, dubbed REDEEM, is accepted by KDD 2025 Round 2. We will release the code for this extension in https://github.com/Jian-Lang/REDEEM.
+ 2024.12.10 🔥 Our work, RAGPT, which aims to tackle the incomplete multimodal learning in pre-trained multimodal transformers, is accepted by AAAI 2025.

## Abstract

Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MMTs) under missing modality conditions by applying learnable prompts. However, these prompt-based methods face several limitations: (1) incomplete modalities provide restricted modal cues for task-specific inference, (2) dummy imputation for missing content causes information loss and introduces noise, and (3) static prompts are instance-agnostic, offering limited knowledge for instances with various missing conditions. To address these issues, we propose **RAGPT**, a novel **R**etrieval-**A**u**G**mented dynamic **P**rompt **T**uning framework. RAGPT comprises three modules: (I) the multi-channel retriever, which identifies similar instances through a withinmodality retrieval strategy, (II) the missing modality generator, which recovers missing information using retrieved contexts, and (III) the context-aware prompter, which captures contextual knowledge from relevant instances and generates dynamic prompts to largely enhance the MMT’s robustness.

## Framework

## Missing Setting

We assume training set is fully available and define the missing rate $\eta$ % as the rate of modality-incomplete samples in the **test set**: (1) text/image missing with $\eta$ % indicates that there are $\eta$ % image-only/text-only instances and (1 - $\eta$ %) modality-complete instances. (2) both modalities missing with $\eta$ % indicates that there are $\frac{\eta}{2}$ % text-only instances, $\frac{\eta}{2}$ % image-only instances and (1 - $\eta$ %) modality-complete instances. We set **missing rate $\eta$ = 70 by default**. For training of each model (both ours and baselines), **we simulate the same 70 % missing rate to align model optimization well with test conditions,** but **allow each model to access the full modality information in the training set** to assist the training process. (e.g., for reconstruction models, we allow them to leverage the full modality information from training set as the reconstruction learning supervision signals). And **our memory bank is only constructed with the training and validation set** for a fair comparison (without any test data leakage).

## Environment Configuration

First, clone this repo:

```shell
git clone https://github.com/Jian-Lang/RAGPT.git

cd RAGPT
```

First, create a new conda env for RAGPT:

```shell
conda create -n RAGPT python=3.9
```

Next, activate this env and install the dependencies from the requirements.txt:

```shell
conda activate RAGPT

pip install -r requirements.txt
```

## Data Preparation

### MM-IMDb

First, download the dataset from this link: https://archive.org/download/mmimdb/mmimdb.tar.gz

Then, place the raw images in folder **dataset/mmimdb/image** and put the json files in folder **dataset/mmimdb/meta_data**.

### HateMemes

First, download the dataset from this link: https://www.kaggle.com/datasets/parthplc/facebook-hateful-meme-dataset

Then, place the raw images in folder **dataset/hatememes/image** and put the json files in folder **dataset/hatememes/metadata**.

Next, replace the **test.json** in metadata with **test_seen.json** downloaded from this link: https://www.kaggle.com/datasets/williamberrios/hateful-memes as the test.json downloaded from the prior website has no label information for evaluation. (Do not change other files, only replace the test.json with test_seen.json)

### Food101

First, download the dataset from this link: https://www.kaggle.com/datasets/gianmarco96/upmcfood101

Then, place the raw images in folder **dataset/food101/image** and put the csv files in folder **dataset/food101/meta_data**.

Note: The image folder follows the structure dataset/xxx/image/xxx.jpg|png|jpeg

## Code Running

### Dataset Initiation

Run the following script to init the dataset:

```shell
sh src/scripts/init_data.sh
```

### Training & Evaluation

Run the following script to training our model and evaluate the results:

```shell
sh src/scripts/eval.sh
```

All the parameters have the same meaning as describe in our paper and you can simply config them in **src/config/config.yaml** or in command line.

Run the following script to training baseline model and evaluate the results:

```shell
sh src/scripts/eval_baseline.sh
```

## Citation

If you find the code useful for your research, please give us a star ⭐⭐⭐ and consider citing:

```
@inproceedings{lang2025retrievalaugmented,
    author = {Lang, Jian and Cheng, Zhangtao and Zhong, Ting and Zhou, Fan},
    booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
    year = {2025},
    pages = {18035--18043},
    doi = {10.1609/aaai.v39i17.33984},
    title = {Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning},
}
```

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