Hateful Memes Challenge and dataset - AI at Meta
WEBWe’re launching the Hateful Memes Challenge, an online competition with a $100K total prize pool, and sharing a dataset designed specifically to help AI researchers develop new systems to identify multimodal hate speech.
Hateful Memes Dataset - Papers With Code
WEBThe Hateful Memes data set is a multimodal dataset for hateful meme detection (image + text) that contains 10,000+ new multimodal examples created by Facebook AI. Images were licensed from Getty Images so that researchers can use the data set to support their work.
Hateful Memes Detection Based on Multi-Task Learning - MDPI
WEBNov 30, 2022 · Hateful memes detection is a binary classification task that uses text and image signals to judge whether a meme is hateful. Our designed model takes I t and I v as inputs after data processing and the hateful intensity y ^ m ∈ R as outputs.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes …
WEB5 days ago · To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes.
MemeTector: enforcing deep focus for meme detection
WEBMay 13, 2023 · In this work, we address the problem of image meme detection. We introduce a novel artificial dataset creation process termed visual part utilization (VPU) that first extracts the visual part of an image meme and then utilizes this new image as an instance of the regular images class.
Caption Enriched Samples for Improving Hateful Memes Detection
WEB5 days ago · Caption Enriched Samples for Improving Hateful Memes Detection - ACL Anthology. Efrat Blaier, Itzik Malkiel, Lior Wolf. Abstract. The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is …
WEBHateful meme detection is a challenging multimodal task that re-quires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task.
WEBHateful meme detection is not only fundamental in gov-erning a healthy online environment, but it is also an impor-tant research topic that requires both visual and linguistic modeling skills and advanced knowledge of effectively com-bining multimodal representations.
WEBWe take an interpretable approach to hateful meme detection, us- ing machine learning and simple heuristics to identify the features most important to classifying a meme as hateful.
Hateful meme detection with multimodal deep neural networks
WEBHateful meme detection with multimodal deep neural networks Abstract: The modern advances of social media platforms and content sharing websites led to the popularization of Internet memes, and today's Internet landscape contains websites that are predominantly dedicated to meme sharing.
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