@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T CYBERPUNK@DravidianLangTech 2026: Multimodal Political Meme Classification using CLIP and Logo Similarity
%A Abir, Shahad
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F abir-2026-cyberpunk
%X 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.
%U https://aclanthology.org/2026.dravidianlangtech-1.31/
%P 222-226
Markdown (Informal)
[CYBERPUNK@DravidianLangTech 2026: Multimodal Political Meme Classification using CLIP and Logo Similarity](https://aclanthology.org/2026.dravidianlangtech-1.31/) (Abir, DravidianLangTech 2026)
ACL