XMeCap: Meme Caption Generation with Sub-Image Adaptability
Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines. While advances have been made in natural language processing, real-world humor often thrives in a multi-modal context, encapsulated distinctively by memes. This paper poses a particular emphasis on...
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| Main Authors | , , , , |
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| Format | Journal Article |
| Language | English |
| Published |
24.07.2024
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| Subjects | |
| Online Access | Get full text |
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| Summary: | Humor, deeply rooted in societal meanings and cultural details, poses a
unique challenge for machines. While advances have been made in natural
language processing, real-world humor often thrives in a multi-modal context,
encapsulated distinctively by memes. This paper poses a particular emphasis on
the impact of multi-images on meme captioning. After that, we introduce the
\textsc{XMeCap} framework, a novel approach that adopts supervised fine-tuning
and reinforcement learning based on an innovative reward model, which factors
in both global and local similarities between visuals and text. Our results,
benchmarked against contemporary models, manifest a marked improvement in
caption generation for both single-image and multi-image memes, as well as
different meme categories. \textsc{XMeCap} achieves an average evaluation score
of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming
the best baseline by 3.71\% and 4.82\%, respectively. This research not only
establishes a new frontier in meme-related studies but also underscores the
potential of machines in understanding and generating humor in a multi-modal
setting. |
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| DOI: | 10.48550/arxiv.2407.17152 |