[Submitted on 17 Sep 2026]
Title:MemeTAG: Keyword-Driven Meme Classification through Tag Embedding Reconstruction
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Abstract:The proliferation of harmful internet memes poses a significant societal threat, yet their automated classification remains a formidable algorithmic challenge due to the nuanced, multimodal nature of their content. To address this, we introduce MemeTAG, a novel dual-objective framework that pioneers a keyword-aware approach to meme classification. Our core innovation is a two-part semantic guidance mechanism: first, we leverage a pretrained Vision-Language Model to generate a set of descriptive keywords, that capture the high-level semantics. Second, we introduce the Aggregated Tag Inference Network (ATIN), an attention-based module that distills these keywords into a single, rich semantic embedding. This embedding serves as a target for a novel auxiliary reconstruction loss, which compels the model to learn deeply aligned visual and textual features. This approach, combined with an efficient three-stage training strategy, establishes a new state-of-the-art on the HarMeme, Hateful Memes Challenge (HMC), and PrideMM datasets, decisively outperforming existing state-of-the-art methods.
Comments: 10 pages, 3 figures; published in the Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
Cite as: arXiv:2609.20962 [cs.CV]
(or arXiv:2609.20962v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.20962
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026, pp. 7679-7688
Related DOI:
https://doi.org/10.1109/WACV61042.2026.00741
DOI(s) linking to related resources
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From: Akshit Sharma [view email] [v1] Thu, 17 Sep 2026 18:17:12 UTC (190 KB)
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