DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Models
Paper
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2202.04053
•
Published
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PaintSkills
- ICCV 2023 Paper: DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
- GitHub Repository: https://github.com/j-min/DallEval
Dataset Summary
PaintSkills is a compositional diagnostic dataset that evaluates three visual reasoning skills (object/count/spatial) of text-to-image generation models.
PaintSkills consist of a set of (text prompt, scene configuration, GT image) for each of three skills, which are collected in three steps:
- We define scene configurations for each skill, in which the objects, attributes, and relations are uniformly distributed.
- We generate text prompts by creating templates with objects, numbers, and spatial relations.
- We generate images from the scene configurations using a 3D simulator.
Skills
- Object Recognition (Object).
- Given a text describing a specific object class (e.g., an airplane), a model generates an image that contains the intended class of object.
- Object Counting (Count).
- Given a text describing M objects of a specific class (e.g., 3 dogs), a model generates an image that contains M objects of that class.
- Spatial Relation Understanding (Spatial).
- Given a text describing two objects having a specific spatial relation (e.g., one is right to another), a model generates an image including two objects with the relation.
Splits
PaintSkills have train and test splits.
The train split has 23,250/21,600/13,500 scenes for object/count/spatial skills.
The test split has 2,325/2,160/2,700 scenes for object/count/spatial skills.
Citation Information
@inproceedings{Cho2023DallEval,
title = {DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models},
author = {Jaemin Cho and Abhay Zala and Mohit Bansal},
year = {2023},
booktitle = {ICCV},
}
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