11 citations · 18 across the 10 of their papers we have counts for
10 papers
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton +7
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 ima…
GRADE: Quantifying Sample Diversity in Text-to-Image Models
Royi Rassin, Aviv Slobodkin, Shauli Ravfogel +2
We introduce GRADE, an automatic method for quantifying sample diversity in text-to-image models. Our method leverages the world knowledge embedded in large language models and vis…
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Sahil Verma, Royi Rassin, Arnav Das +6
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models…
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models
Nitzan Bitton-Guetta, Aviv Slobodkin, Aviya Maimon +6
Imagine observing someone scratching their arm; to understand why, additional context would be necessary. However, spotting a mosquito nearby would immediately offer a likely expla…
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
Lital Binyamin, Yoad Tewel, Hilit Segev +3
Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various appli…
Evaluating D-MERIT of Partial-annotation on Information Retrieval
Royi Rassin, Yaron Fairstein, Oren Kalinsky +4
Retrieval models are often evaluated on partially-annotated datasets. Each query is mapped to a few relevant texts and the remaining corpus is assumed to be irrelevant. As a result…
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Jiao Sun, Deqing Fu, Yushi Hu +8
Despite their wide-spread success, Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user's input text. We introd…
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
Royi Rassin, Eran Hirsch, Daniel Glickman +3
Text-conditioned image generation models often generate incorrect associations between entities and their visual attributes. This reflects an impaired mapping between linguistic bi…
Conjunct Resolution in the Face of Verbal Omissions
Royi Rassin, Yoav Goldberg, Reut Tsarfaty
Verbal omissions are complex syntactic phenomena in VP coordination structures. They occur when verbs and (some of) their arguments are omitted from subsequent clauses after being…
DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models
Royi Rassin, Shauli Ravfogel, Yoav Goldberg
We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image). We show that in stark contrast to the w…