Nitzan Bitton Guetta — accepted AI-conference papers

2025

RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation

Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton, Brian Gordon, Michal Sokolik, Nitzan Bitton Guetta +4

EMNLP 2025

Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability—ranging from enhanced personalization in image generation to consistent c

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2024

Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models

Nitzan Bitton Guetta, Aviv Slobodkin, Aviya Maimon, Eliya Habba, Royi Rassin, Yonatan Bitton +3

NeurIPS 2024poster

Imagine observing someone scratching their arm; to understand why, additional context would be necessary. However, spotting a mosquito nearby would immediately offer a likely explanation for the person’s discomfort, thereby alleviating the need for further information. This example illustrates how s…

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2022

WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models

Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef, Yuval Elovici, Mohit Bansal, Gabriel Stanovsky +1

NeurIPS 2022accept

While vision-and-language models perform well on tasks such as visual question answering, they struggle when it comes to basic human commonsense reasoning skills. In this work, we introduce WinoGAViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used…

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