Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting
We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) and short-form videos (Task 3). Our system follows a feature-engineered late-fusion pipeline built around gradient-boosted regression models and hierarchical post-processing. For memes, we combine visual, textual, demographic, biometric, and LLM-derived semantic indicators designed to capture high-level cues such as stereotyping, objectification, irony, and misogyny. For videos, we investigate the effect of feature selection, frame-based visual representations, OCR-based textual features, acoustic descriptors, and sensor-derived metadata. De...
| Authors | Kyriakos Chaviaras, Maria Lymperaiou, Athanasios Voulodimos |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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