CUET_SYNTHETICA@EEUCA 2026: Gated Cross-Modal Attention with Domain-Adapted Text Encoding for Vaccine-Critical Meme Detection
Year: 2026
Type: conference-paper
Abstract: Vaccine-critical memes have emerged as a growing challenge for public health communication, combining images and text to spread misinformation in ways that are difficult to detect automatically.In this paper, we have described our system for the EEUCA 2026 Shared Task on Multimodal Vaccine-Critical Meme Detection, classifying memes from the VaxMeme dataset into Vaccine-Critical, Neutral and Pro-Vaccine categories.We have experimented with multiple text encoders and visual backbones, finding that Twitter-RoBERTa fused with CLIP ViT-L/14 through gated crossmodal attention has achieved a test macro F1 of 0.8357.We have further shown that domainspecific pretraining has outperformed larger general-purpose models, highlighting the importance of domain adaptation over raw model scale.Finally, our system has secured the 3rd position on the shared task leaderboard.
Institution: Chittagong University of Engineering & Technology
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166888539