86 citations · 107 across the 10 of their papers we have counts for
12 papers
ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts
Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi +5
ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while T…
AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Abul Hasnat +3
Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implic…
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…
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…
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…
ArMeme: Propagandistic Content in Arabic Memes
Firoj Alam, Abul Hasnat, Fatema Ahmed +2
With the rise of digital communication, memes have become a significant medium for cultural and political expression that is often used to mislead audiences. Identification of such…
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
Firoj Alam, Tanvirul Alam, Md. Arid Hasan +3
Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based…
DeepVisage: Making face recognition simple yet with powerful generalization skills
Abul Hasnat, Julien Bohné, Jonathan Milgram +2
Face recognition (FR) methods report significant performance by adopting the convolutional neural network (CNN) based learning methods. Although CNNs are mostly trained by optimizi…
Spectral video construction from RGB video: Application to Image Guided Neurosurgery
Md. Abul Hasnat, Jussi Parkkinen, Markku Hauta-Kasari
Spectral imaging has received enormous interest in the field of medical imaging modalities. It provides a powerful tool for the analysis of different organs and non-invasive tissue…
Opinion mining from twitter data using evolutionary multinomial mixture models
Md. Abul Hasnat, Julien Velcin, Stéphane Bonnevay +1
Image of an entity can be defined as a structured and dynamic representation which can be extracted from the opinions of a group of users or population. Automatic extraction of suc…
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study
Md. Abul Hasnat, Julien Velcin, Stéphane Bonnevay +1
In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises seve…