@inproceedings{shen-2026-wenbin,
    title = "wenbin@{EEUCA} 2026: {M}o{E}s-{V}ax{A}gent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection",
    author = "Shen, Wenbin",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.15/",
    pages = "141--150",
    ISBN = "979-8-89176-402-6",
    abstract = "Memes on social media have emerged as a crucial medium for disseminating vaccine-related viewpoints, yet their inherent irony, metaphor, and text-image misalignment pose significant challenges to automatic detection. In this paper, we propose MoEs-VaxAgent, a two-stage multimodal framework for vaccine critical meme detection. First, we design a dynamic routing Mixture-of-Experts module capable of adaptively capturing multi-granular semantic cues within memes. Second, to address hard samples located at the decision boundaries, we introduce an uncertainty-aware multi-agent rectification mechanism to perform a secondary detection on samples identified with low confidence in the first stage. In the EEUCA 2026 Shared Task on Multimodal Vaccine Critical Meme Detection, our system achieved a Macro F1-score of 0.8205, ranking 9th on the official leaderboard. Furthermore, we discuss various exploratory strategies evaluated during the competition and provide a detailed analysis of the model{'}s performance."
}