From the 1 of 11 linked papers with an AI index.
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AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Abul Hasnat +3
The paper presents AHA-Memes, a large-scale Arabic hateful meme dataset with fine-grained, multi‑label annotations, and provides baseline evaluations of text, image, and multimodal…
Adapting Reinforcement Learning with Chain-of-Thought Supervision for Explainable Detection of Hateful and Propagandistic Memes
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone. Although thinking-based multimodal larg…
OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA
Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan +8
Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries require cultural and visual information,…
Can Thinking Models Think to Detect Hateful Memes?
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful memes often require compositional multimodal reasoning: the image and text may appear benign in isolation, yet their interaction conveys harmful intent. Although thinking-b…
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes
Mohamed Bayan Kmainasi, Abul Hasnat, Md Arid Hasan +2
The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, ha…
PropXplain: Can LLMs Enable Explainable Propaganda Detection?
Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi +4
There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with l…