@inproceedings{arunachalam-etal-2025-misogynistic,
title = "Misogynistic Meme Detection in {D}ravidian Languages Using Kolmogorov Arnold-based Networks",
author = "Arunachalam, Manasha and
Chukka, Navneet Krishna and
V, Harish Vijay and
B, Premjith and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.24/",
doi = "10.18653/v1/2025.dravidianlangtech-1.24",
pages = "144--151",
ISBN = "979-8-89176-228-2",
abstract = "The prevalence of misogynistic content online poses significant challenges to ensuring a safe and inclusive digital space for women. This study presents a pipeline to classify online memes as misogynistic or non misogynistic. The pipeline combines contextual image embeddings generated using the Vision Transformer Encoder (ViTE) model with text embeddings extracted from the memes using ModernBERT. These multimodal embeddings were fused and trained using three advanced types of Kolmogorov Artificial Networks (KAN): PyKAN, FastKAN, and Chebyshev KAN. The models were evaluated based on their F1 scores, demonstrating their effectiveness in addressing this issue. This research marks an important step towards reducing offensive online content, promoting safer and more respectful interactions in the digital world."
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%0 Conference Proceedings
%T Misogynistic Meme Detection in Dravidian Languages Using Kolmogorov Arnold-based Networks
%A Arunachalam, Manasha
%A Chukka, Navneet Krishna
%A V, Harish Vijay
%A B, Premjith
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F arunachalam-etal-2025-misogynistic
%X The prevalence of misogynistic content online poses significant challenges to ensuring a safe and inclusive digital space for women. This study presents a pipeline to classify online memes as misogynistic or non misogynistic. The pipeline combines contextual image embeddings generated using the Vision Transformer Encoder (ViTE) model with text embeddings extracted from the memes using ModernBERT. These multimodal embeddings were fused and trained using three advanced types of Kolmogorov Artificial Networks (KAN): PyKAN, FastKAN, and Chebyshev KAN. The models were evaluated based on their F1 scores, demonstrating their effectiveness in addressing this issue. This research marks an important step towards reducing offensive online content, promoting safer and more respectful interactions in the digital world.
%R 10.18653/v1/2025.dravidianlangtech-1.24
%U https://aclanthology.org/2025.dravidianlangtech-1.24/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.24
%P 144-151
Markdown (Informal)
[Misogynistic Meme Detection in Dravidian Languages Using Kolmogorov Arnold-based Networks](https://aclanthology.org/2025.dravidianlangtech-1.24/) (Arunachalam et al., DravidianLangTech 2025)
ACL
- Manasha Arunachalam, Navneet Krishna Chukka, Harish Vijay V, Premjith B, and Bharathi Raja Chakravarthi. 2025. Misogynistic Meme Detection in Dravidian Languages Using Kolmogorov Arnold-based Networks. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 144–151, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.