HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes

TL;DR

HateSieve is a novel contrastive learning framework that improves detection and segmentation of hateful content in memes, addressing limitations of current safety measures in multimodal content analysis.

Contribution

It introduces a contrastive meme generator, a triplet dataset, and an image-text alignment module for precise hateful content identification in memes.

Findings

Outperforms existing models on the Hateful Meme Dataset

Uses fewer trainable parameters for comparable or better performance

Provides accurate segmentation of hateful elements in memes

Abstract

Amidst the rise of Large Multimodal Models (LMMs) and their widespread application in generating and interpreting complex content, the risk of propagating biased and harmful memes remains significant. Current safety measures often fail to detect subtly integrated hateful content within ``Confounder Memes''. To address this, we introduce \textsc{HateSieve}, a new framework designed to enhance the detection and segmentation of hateful elements in memes. \textsc{HateSieve} features a novel Contrastive Meme Generator that creates semantically paired memes, a customized triplet dataset for contrastive learning, and an Image-Text Alignment module that produces context-aware embeddings for accurate meme segmentation. Empirical experiments on the Hateful Meme Dataset show that \textsc{HateSieve} not only surpasses existing LMMs in performance with fewer trainable parameters but also offers a robust mechanism for precisely identifying and isolating hateful content. \textcolor{red}{Caution: Contains academic discussions of hate speech; viewer discretion advised.}