@inproceedings{rao-etal-2025-code,
title = "{C}ode{\_}{C}onquerors@{D}ravidian{L}ang{T}ech 2025: Multimodal Misogyny Detection in {D}ravidian Languages Using Vision Transformer and {BERT}",
author = "Rao, Pathange Omkareshwara and
V, Harish Vijay and
Srichandra, Ippatapu Venkata and
Mohan, Neethu and
S, Sachin Kumar",
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-1.49/",
doi = "10.18653/v1/2025.dravidianlangtech-1.49",
pages = "283--287",
ISBN = "979-8-89176-228-2",
abstract = "This research focuses on misogyny detection in Dravidian languages using multimodal techniques. It leverages advanced machine learning models, including Vision Transformers (ViT) for image analysis and BERT-based transformers for text processing. The study highlights the challenges of working with regional datasets and addresses these with innovative preprocessing and model training strategies. The evaluation reveals significant improvements in detection accuracy, showcasing the potential of multimodal approaches in combating online abuse in underrepresented languages."
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%0 Conference Proceedings
%T Code_Conquerors@DravidianLangTech 2025: Multimodal Misogyny Detection in Dravidian Languages Using Vision Transformer and BERT
%A Rao, Pathange Omkareshwara
%A V, Harish Vijay
%A Srichandra, Ippatapu Venkata
%A Mohan, Neethu
%A S, Sachin Kumar
%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 rao-etal-2025-code
%X This research focuses on misogyny detection in Dravidian languages using multimodal techniques. It leverages advanced machine learning models, including Vision Transformers (ViT) for image analysis and BERT-based transformers for text processing. The study highlights the challenges of working with regional datasets and addresses these with innovative preprocessing and model training strategies. The evaluation reveals significant improvements in detection accuracy, showcasing the potential of multimodal approaches in combating online abuse in underrepresented languages.
%R 10.18653/v1/2025.dravidianlangtech-1.49
%U https://aclanthology.org/2025.dravidianlangtech-1.49/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.49
%P 283-287
Markdown (Informal)
[Code_Conquerors@DravidianLangTech 2025: Multimodal Misogyny Detection in Dravidian Languages Using Vision Transformer and BERT](https://aclanthology.org/2025.dravidianlangtech-1.49/) (Rao et al., DravidianLangTech 2025)
ACL
- Pathange Omkareshwara Rao, Harish Vijay V, Ippatapu Venkata Srichandra, Neethu Mohan, and Sachin Kumar S. 2025. Code_Conquerors@DravidianLangTech 2025: Multimodal Misogyny Detection in Dravidian Languages Using Vision Transformer and BERT. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 283–287, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.