@inproceedings{abir-2026-cyberpunk,
    title = "{CYBERPUNK}@{D}ravidian{L}ang{T}ech 2026: Multimodal Political Meme Classification using {CLIP} and Logo Similarity",
    author = "Abir, Shahad",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.31/",
    pages = "222--226",
    ISBN = "979-8-89176-401-9",
    abstract = "We present our system for the DravidianLangTech 2026 shared task on multi-level political meme classification in Tamil and Malayalam. The task involves two hierarchical levels: (1) stance detection (Support vs. Troll) and (2) target identification (Person, Party, or Intersection). Our approach combines CLIP vision-language embeddings (ViT-L-14) with face detection features and political logo similarity matching, resulting in a 773-dimensional feature representation. We train separate LinearSVC classifiers for each language and task level. Our system achieved Rank 1 in Malayalam with an average F1-score of 0.7930 and Rank 6 in Tamil with 0.7666. Our codes are available at https://github.com/A-k-a-sh/Shared-task-multimodal-political-meme."
}