From the 1 of 10 linked papers with an AI index.
10 papers
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…
CritiSense: Critical Digital Literacy and Resilience Against Misinformation
Firoj Alam, Fatema Ahmad, Ali Ezzat Shahroor +5
Misinformation on social media undermines informed decision-making and public trust. Prebunking offers a proactive complement by helping users recognize manipulation tactics before…
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,…
MemeLens: Multilingual Multitask VLMs for Memes
Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Abul Hasnat +4
Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme…
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…