RT Conference Proceedings
T1 I2C-UHU-Altair at EXIST2025: Multimodal Sexism Detection and Classification Using Advanced Vision-Language Models BLIP2 and Qwen, Large Language Models, and Learning with Disagreement Frameworks
T2 Detection and Classification Using Advanced Vision-Language Models BLIP2 and Qwen, Large Language Models, and Learning with Disagreement Frameworks
A1 Guerrero García, Manuel
A1 Carrillo García, Fernando
A1 Mata Vázquez, Jacinto
A1 Pachón Álvarez, Victoria
K1 Multimodal Learning
K1 Vision-Language Models
K1 Sexism Detection
K1 Learning with Disagreement
K1 Large Language Models
K1 Social Media Analysis
AB In this paper, we present the contributions of the I2C-UHU-Altair team to the EXIST2025 Lab at CLEF 2025, addressing the identification and classification of sexism in multimodal online content, particularly memes. Our system leverages recent advances in large language models (LLMs) and vision-language models (VLMs) to process both textual and visual information in a unified manner. We tackle three subtasks: binary classification of memes as sexist or non-sexist, classification of the author’s intent behind the meme, and multi-label categorization of sexist content. To enhance model robustness, we adopt the Learning with Disagreement framework, allowing the system to benefit from divergent annotations that reflect the inherent ambiguity and subjectivity in sociolinguistic tasks. We detail our multimodal architecture, preprocessing pipeline, and fine-tuning strategy. Our system demonstrated competitive performance in the shared task, achieving notable positions across all subtasks. Specifically, we ranked 5th in Subtask 2.1 (Soft-Soft), 3rd in Subtask 2.2 (Soft-Soft), and 3rd and 6th in Subtask 2.3 (Soft-Soft and Hard-Hard, respectively). Our findings highlight the potential of multimodal learning for detecting nuanced expressions of sexism in online environments and open avenues for future research in social media moderation and fairness-aware NLP.
PB CEUR-WS.org
SN 1613-0073
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27976
UL https://hdl.handle.net/10272/27976
LA eng
NO Guerrero-García, M., Carrillo-García, F., Vázquez, J.M., & Pachón, V. (2025). I2C-UHU-Altair at EXIST2025: Multimodal Sexism Detection and Classification Using Advanced Vision-Language Models BLIP2 and Qwen, Large Language Models, and Learning with Disagreement Frameworks. Conference and Labs of the Evaluation Forum.
DS Repositorio Institucional de la Universidad de Huelva
RD 10 may 2026