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[NeurIPS'20-Competition] Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge. https://arxiv.org/abs/2012.12975

Hate-CLIPper: Multimodal Hateful Meme Classification with Explicit Cross-modal Interaction of CLIP features - Accepted at EMNLP 2022 Workshop

This project addresses the growing challenge of detecting hateful memes across multiple languages on social media platforms. Hateful memes combine text and images to convey offensive messages that target individuals or groups based on characteristics such as race, gender, ethnicity, and religion.

📄 ACL 2024: RGCL, Retrieval-Guided Contrastive Learning for Hateful Meme Detection 📄 EMNLP 2025 (Oral): RA-HMD, Robust Adaptation of Large Multimodal Models for Retrieval-Augmented Hateful Meme Detection Official implementation with pretrained models and reproduction scripts.

[ICCVW 2023] - Mapping Memes to Words for Multimodal Hateful Meme Classification

Fine grained annotations extending hateful memes dataset with additional labels for identifying protected categories and attack types.

Repository containing code from team Kingsterdam for the Hateful Memes Challenge

Dataset and code implementation for the paper "Decoding the Underlying Meaning of Multimodal Hateful Memes" (IJCAI'23).

[COLING 2025🔥] Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection

Facebook hateful memes challenge using multi-modal learning. More info about it here: https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set

The Hateful Memes Challenge example code using MMF

Official repository for ACM Multimedia'23 paper "Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection"

A Multimodal Deep-Learning-based Project aimed to classify whether the given meme is hateful or not

[ACL, EACL'24] Dataset and PyTorch Code for Multimodal Hate Speech Detection in Bengali

Official repository for WWW'24 paper "Modularized Networks for Few-shot Hateful Meme Detection"

11-777: Multimodal Machine Learning Course Project Repository

Multimodal (Visuals & Language) hateful memes detection: on Hateful memes challenge dataset

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MemeGraphs: Linking memes to knowledge graphs for hateful memes classification

Social Computing Project

Hateful Meme Detection By Leveraging SOTA Visuo-Linguistic Models

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Joint work on utilizing a combination of NLP and CV methods in implementing multimodal approaches to combat hate speech.

This project addresses the growing challenge of detecting hateful memes across multiple languages on social media platforms. Hateful memes combine text and images to convey offensive messages that target individuals or groups based on characteristics such as race, gender, ethnicity, and religion.

Enhance Multimodal Model Performance with Data Augmentation: Facebook Hateful Meme Challenge Solution

Hateful meme detection is a well-known research area that requires both visual and lin- guistic understanding. It matters because in today’s world information and opinions stem from multimedia. With people smartly disguising hateful intent behind apparently harmless images/text which when combined within cultural and societal context can hurt sentiments of various minority groups. Thus, there is a dire need to be able to detect such hateful multimedia in a multimodal setting. For this purpose, we have used Facebook’s hate meme detection data set specially anno- tated such that the unimodal priors are bound to fail, that is, the images and text individually don’t hold much signal. We have used ResNext and RoBERTa unimodal models as the base- lines. In order to explore the multimodality of the dataset, we used the early fusion approach by concatenating the ResNext embeddings of pure images (2047 dimensional) and RoBERTa embeddings of text (768 dimensional) and then subsequently performing classification using various fine-tuned models such as Shallow Feed Forward Network, Deep Feed Forward Net- work, CatBoost, LGBM, XGBoost and Logistic Regression.