| title | AI Wizards at EXIST 2026: Hierarchical Soft-Label Learning for Multimodal Sexism Identification in Memes |
|---|---|
| authors | Matteo Fasulo, Antonio Gravina, Luca Tedeschini, Luca Babboni |
| date | 2026-07-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.04410 |
| affiliation | AI Wizards |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We present the AI Wizards submission to EXIST 2026 for multimodal sexism identification in memes. The task is composed of three, increasingly harder subtasks. We model them hierarchically as conditional soft-label prediction over empirical annotator distributions. Our system maps fixed Gemini Embedding 2 vision-language representations through a lightweight Gated MLP trained with KL divergence and homoscedastic uncertainty weighting. Our submissions ranked first on Task 2.3 and fourth on Tasks 2.1 and 2.2 on the official Soft-Soft leaderboards. The code is available at https://github.com/NLP-AI-Wizards/EXIST-2026 |
cite
@misc{fasulo2026ai,
title={AI Wizards at EXIST 2026: Hierarchical Soft-Label Learning for Multimodal Sexism Identification in Memes},
author={Matteo Fasulo and Antonio Gravina and Luca Tedeschini and Luca Babboni},
year={2026},
eprint={2607.04410},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.04410}
}