A CLIP-based siamese approach for meme classification
<|reference_start|>A CLIP-based siamese approach for meme classification: Memes are an increasingly prevalent element of online discourse in social networks, especially among young audiences. They carry ideas and messages that range from humorous to hateful, and are widely consumed. Their potentially high impact requires adequate means of control to moderate their use in large scale. In this work, we propose SimCLIP a deep learning-based architecture for cross-modal understanding of memes, leveraging a pre-trained CLIP encoder to produce context-aware embeddings and a Siamese fusion technique to capture the interactions between text and image. We perform an extensive experimentation on seven meme classification tasks across six datasets. We establish a new state of the art in Memotion7k with a 7.25% relative F1-score improvement, and achieve super-human performance on Harm-P with 13.73% F1-Score improvement. Our approach demonstrates the potential for compact meme classification models, enabling accurate and efficient meme monitoring. We share our code at https://github.com/jahuerta92/meme-classification-simclip<|reference_end|>
@article{huertas-tato2024a,
    title={A CLIP-based siamese approach for meme classification},
    author={Javier Huertas-Tato, Christos Koutlis, Symeon Papadopoulos, David
  Camacho, and Ioannis Kompatsiaris},
    journal={arXiv preprint arXiv:2409.05772},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05772},
    primaryClass={cs.MM}
}