arXiv:2012.07788
공식 코드 · Papers with Code
Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes
원문 초록
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.
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Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
Prompt-enhanced Network for Hateful Meme Classification
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
MATK: The Meme Analytical Tool Kit
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