Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification
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Computer Science > Multimedia
arXiv:2609.26907 (cs)
[Submitted on 22 Sep 2026]
Title:Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification
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Abstract:Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal classifiers can miss such evidence when relying mainly on global image-text representations. We introduce MemeCF, a cue-focused benchmark of 9,895 memes across harm, hate, and sarcasm, with annotations identifying the modality and rationale of the pivotal evidence. We also propose MemePIVOT, a local-global architecture for meme classification. MemePIVOT uses frozen CLIP features, unbalanced optimal transport to align words with image patches while allowing irrelevant evidence to remain unmatched, and an evidential fusion head to combine local grounding with global meme context under uncertainty. Experiments on HarMeme, PrideMM, and MemeCF show consistent gains over strong text-only, image-only, multimodal, and vision-language baselines. Cross-dataset and ablation results further show that explicit pivotal-evidence modeling improves robustness and contributes meaningfully beyond global multimodal representations. Our code and dataset are publicly available at this https URL
| Comments: | Accepted to EMNLP 2026 Findings |
| Subjects: | Multimedia (cs.MM); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.26907 [cs.MM] |
| (or arXiv:2609.26907v1 [cs.MM] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26907
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification, by Akshit Sharma and 1 other authors
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