Duplicate from Jingbiao/RA-HMD

Co-authored-by: Mei <Jingbiao@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-text-to-text
+tags:
+- hateful-memes
+- multimodal
+- retrieval-augmented-generation
+- lmm
+---
+
+# Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection Datasets
+
+This repository contains the datasets used in the paper [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).
+
+[Project Page](https://rgclmm.github.io/) | [Code](https://github.com/JingbiaoMei/RGCL)
+
+## Abstract
+Recent advances in Large Multimodal Models (LMMs) have shown promise in hateful meme detection, but face challenges like sub-optimal performance and limited out-of-domain generalization. This work proposes a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. Our approach achieves improved robustness under adversarial attacks compared to supervised fine-tuning (SFT) models and state-of-the-art performance on six meme classification datasets, outperforming larger agentic systems. Additionally, our method generates higher-quality rationales for explaining hateful content, enhancing model interpretability.
+
+## Dataset Preparation
+The datasets consist of image data and corresponding annotation data.
+
+### Image data
+Copy images into `./data/image/dataset_name/All` folder.
+For example: `./data/image/FB/All/12345.png`, `./data/image/HarMeme/All`, `./data/image/Propaganda/All`, etc..
+
+### Annotation data
+Copy `jsonl` annotation file into `./data/gt/dataset_name` folder.
+
+## Sample Usage
+To generate CLIP embeddings for the datasets prior to training, you can use the provided script as follows:
+
+```shell
+python3 src/utils/generate_CLIP_embedding_HF.py --dataset "FB"
+python3 src/utils/generate_CLIP_embedding_HF.py --dataset "HarMeme"
+```
+
+Similarly, to generate ALIGN embeddings:
+
+```shell
+python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "FB"
+python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "HarMeme"
+```
+
+## Citation
+If our work helped your research, please kindly cite our papers:
+
+```
+@inproceedings{RGCL2024Mei,
+    title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",
+    author = "Mei, Jingbiao  and
+      Chen, Jinghong  and
+      Lin, Weizhe  and
+      Byrne, Bill  and
+      Tomalin, Marcus",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.291",
+    doi = "10.18653/v1/2024.acl-long.291",
+    pages = "5333--5347"
+}
+
+ @article{RAHMD2025Mei, title={Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection},
+          url={http://arxiv.org/abs/2502.13061},
+          DOI={10.48550/arXiv.2502.13061},
+          note={arXiv:2502.13061 [cs]},
+          number={arXiv:2502.13061},
+          publisher={arXiv},
+          author={Mei, Jingbiao and Chen, Jinghong and Yang, Guangyu and Lin, Weizhe and Byrne, Bill},
+          year={2025},
+          month=may }
+```

data/FB/FB-HatefulMemes-Images.zip

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