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…
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…