H
Hamed Firooz
Researcher at Facebook
Publications - 36
Citations - 529
Hamed Firooz is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Modality (human–computer interaction). The author has an hindex of 6, co-authored 19 publications receiving 202 citations. Previous affiliations of Hamed Firooz include University of Washington & Microsoft.
Papers
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Proceedings Article
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela,Hamed Firooz,Aravind Mohan,Vedanuj Goswami,Amanpreet Singh,Pratik Ringshia,Davide Testuggine +6 more
TL;DR: This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, constructed such that unimodal models struggle and only multimodAL models can succeed.
Posted Content
Supervised Multimodal Bitransformers for Classifying Images and Text
TL;DR: This work introduces a supervised multimodal bitransformer model that fuses information from text and image encoders, and obtains state-of-the-art performance on various multi-modal classification benchmark tasks, outperforming strong baselines, including on hard test sets specifically designed to measure multimodals performance.
Posted Content
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela,Hamed Firooz,Aravind Mohan,Vedanuj Goswami,Amanpreet Singh,Pratik Ringshia,Davide Testuggine +6 more
TL;DR: The authors proposed a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, where difficult examples are added to the dataset to make it hard to rely on unimodal signals.
Proceedings ArticleDOI
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
Dimitar Dimitrov,Bishr Bin Ali,Shaden Shaar,Firoj Alam,Fabrizio Silvestri,Hamed Firooz,Preslav Nakov,Giovanni Da San Martino +7 more
TL;DR: The SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images as mentioned in this paper focused on memes and had three subtasks: detecting the techniques in the text, detecting the text spans where the techniques are used, and detecting the entire meme.
Proceedings ArticleDOI
Detecting and Understanding Harmful Memes: A Survey
Shivam Sharma,Mashiul Alam,Md. Shad Akhtar,Dimitar I. Dimitrov,Gianvito Martino,Hamed Firooz,Alon Halevy,Fabrizio Silvestri,Preslav Nakov,Tanmoy Chakraborty +9 more
TL;DR: A new typology of harmful memes is proposed based on a systematic analysis of recent literature, and several open-ended aspects such as delineating online harm and empirically examining related frameworks and assistive interventions are presented, which are believed to motivate and drive future research.