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Found 10 papers, 3 papers with code
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
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.
GRADE: Quantifying Sample Diversity in Text-to-Image Models
GRADE leverages the world knowledge embedded in large language models and visual question-answering systems to identify relevant concept-specific axes of diversity (e. g., ``shape'' and ``color'' for the concept ``cookie'').
How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation Threshold
We seek to determine the point at which a model was trained on enough instances to imitate a concept -- the imitation threshold.
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models
To study these skills, we present Visual Riddles, a benchmark aimed to test vision and language models on visual riddles requiring commonsense and world knowledge.
Evaluating D-MERIT of Partial-annotation on Information Retrieval
We show that evaluating on a dataset containing annotations for only a subset of the relevant passages might result in misleading ranking of the retrieval systems and that as more relevant texts are included in the evaluation set, the rankings converge.
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation.
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Then, it uses two VLMs to select the best generation: a Visual Question Answering model that measures the alignment of generated images to the text, and another that measures the generation's aesthetic quality.
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
This reflects an impaired mapping between linguistic binding of entities and modifiers in the prompt and visual binding of the corresponding elements in the generated image.
Conjunct Resolution in the Face of Verbal Omissions
In this work we propose a conjunct resolution task that operates directly on the text and makes use of a split-and-rephrase paradigm in order to recover the missing elements in the coordination structure.
DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models
We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image).
