Year: 2025
Type: dissertation
Abstract: This thesis focuses on detecting harmful content in memes using advanced machine learning methods. It begins with a literature review, identifying the strengths, weaknesses, and challenges of current approaches while introducing a new taxonomy to facilitate method comparison. The research presents an improvement to canonical multimodal transformer models by integrating Compact Parameter Blocks into the encoder segments, achieving superior performance compared to more complex techniques. Additionally, it explores the use of generative models, such as Multimodal Large Language Models (MLLMs), to detect aggressive memes through specific prompts. The results indicate that while these models can identify harmful content, their performance declines when high-level multimodal reasoning is required. This research contributes to the field by enhancing detection methods and exploring new generative model-based approaches, aiming to create safer online environments while preserving freedom of expression.
Author: Queiroz Hermida, Paulo Cezar de
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7120696823