Mapping Memes to Words for Multimodal Hateful Meme Classification (2310.08368v1)

Abstract: Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions. However, some memes take a malicious turn, promoting hateful content and perpetuating discrimination. Detecting hateful memes within this multimodal context is a challenging task that requires understanding the intertwined meaning of text and images. In this work, we address this issue by proposing a novel approach named ISSUES for multimodal hateful meme classification. ISSUES leverages a pre-trained CLIP vision-LLM and the textual inversion technique to effectively capture the multimodal semantic content of the memes. The experiments show that our method achieves state-of-the-art results on the Hateful Memes Challenge and HarMeme datasets. The code and the pre-trained models are publicly available at https://github.com/miccunifi/ISSUES.

Mapping Memes to Words for Multimodal Hateful Meme Classification

The paper presented in the paper "Mapping Memes to Words for Multimodal Hateful Meme Classification" proposes an innovative approach to the challenge of classifying hateful memes, which are a unique form of digital communication that intertwines text and imagery. The proposed methodology, termed ISSUES, integrates the CLIP vision-LLM augmented with textual inversion, thereby enhancing semantic content extraction from memes in a multimodal context.

Introduction and Problem Definition

The combined use of images and text in memes presents a significant challenge for automatic classification, as the meaning often emerges from the interaction between these elements. The paper discusses the challenge of detecting harmful content within these composites, particularly when memes perpetuate discriminatory ideas. Previous attempts to tackle this issue, such as those undertaken in Facebook's Hateful Memes Challenge, underscore the necessity of multimodal approaches to disentangle the intricacies of hateful content.

Methodology

ISSUES leverages a pre-trained CLIP model, notable for its ability to align visual and textual data into a shared embedding space. A distinctive contribution of the paper is the application of textual inversion, which involves mapping images into a pseudo-word token within the CLIP token embedding space. This technique enriches the textual representation with visual information, thereby creating a multimodal embedding that enhances meme content understanding.

The architecture incorporates linear projections to disentangle image and text features, adapting both embedding spaces to optimize for the task at hand. The paper introduces a two-stage training process, initially pre-training the visual encoder and subsequently using a Combiner network for multimodal fusion. This approach facilitates better modeling of semantic interactions between text and image representations.

Experimental Results

The paper demonstrates the effectiveness of ISSUES through experiments on the Hateful Memes Challenge (HMC) dataset and the HarMeme dataset. ISSUES outperforms current state-of-the-art models, including Hate-CLIPper, significantly increasing both accuracy and AUROC metrics. The incorporation of textual inversion results in notable improvements, underscoring its efficacy in enhancing multimodal representation.

Implications and Future Directions

The advancements introduced by ISSUES have practical implications in content moderation on digital platforms, providing a more nuanced detection of hateful memes that traditional uni-modal models might overlook. Theoretically, the paper highlights the potential of textual inversion in multimodal classification tasks, opening avenues for further exploration in similar contexts, such as automated content curation and sentiment analysis.

Future research could explore the scalability of ISSUES on larger datasets and its adaptability to other forms of multimodal content beyond memes. Additionally, investigating the integration of further advanced fusion techniques may enhance the system's robustness in various real-world applications.

In conclusion, the paper presents a comprehensive and insightful contribution to multimodal meme classification, addressing critical challenges and setting a foundation for future developments in AI-driven content analysis.