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Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes
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This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset. The implemented models have been fitted into a uniform code-base and altered to yield better performance. The goal of Vilio is to provide a user-friendly starting point for any visio-linguistic problem. An ensemble of 5 different V+L models implemented in Vilio achieves 2nd place in the Hateful Memes Challenge out of 3,300 participants. The code is available at https://github.com/Muennighoff/vilio.
Forward citations
Cited by 3 Pith papers
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Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes
MAR-12 improves humor and hate detection in memes by prompting a VLM through twelve reasoning perspectives, attention-weighting them, and generating explanations from the weighted evidence.
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Unpacking Hateful Memes: Presupposed Context and False Claims
A hateful-meme detector that combines presupposed-context fusion, LLM-based social perception, and cross-modal reference graphs outperforms prior models on three benchmarks and transfers to fake news.
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MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
MIND uses unlabeled similar memes, bidirectional AI insight derivation, and multi-agent debate to improve zero-shot harmful meme detection on HarM, FHM, and MAMI.
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