Unimodal Intermediate Training for Multimodal Meme Sentiment Classification: Architectural Details

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Unimodal Intermediate Training for Multimodal Meme Sentiment Classification: Architectural Details
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Explore the impact of unimodal training on enhancing meme sentiment analysis and strategies in multimodal meme classifiers for improved performance.

Authors: Muzhaffar Hazman, University of Galway, Ireland; Susan McKeever, Technological University Dublin, Ireland; Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs.

and we similarly utilise the Image and Text Encoders from the pretrained ViT–B/16 CLIP model to generate representations of each modality. FI=ImageEncoder FT=T extEncoder Where each and is a 512-digit embedding of the image and text modalities, respectively, from CLIP’s embedding space that aligns images with their corresponding text captions . FI FT For unimodal inputs, the encoder for the missing modality is fed a blank input, i.e.

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