Summary of Improving Explicit Spatial Relationships in Text-to-image Generation Through An Automatically Derived Dataset, by Ander Salaberria et al.
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
by Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle, Aitor Soroa, Eneko Agirre, Frank Keller
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method is proposed to generate synthetic captions that contain explicit spatial relations between objects, aiming to improve current text-to-image systems’ accuracy in reflecting such relations. The Spatial Relation for Generation (SR4G) dataset is introduced, containing 9.9 million image-caption pairs and over 60 thousand captions for evaluation. To test generalization, an ‘unseen’ split is provided where the object sets in train and test captions are disjoint. Fine-tuning two Stable Diffusion models (SD_{SR4G}) on SR4G yields up to 9 points improvements in the VISOR metric, with consistent improvement across all relations. SD_{SR4G} achieves state-of-the-art performance with fewer parameters and avoids complex architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Existing text-to-image systems struggle to accurately reflect explicit spatial relations between objects, like “left of” or “below”. To fix this, scientists created a new way to generate synthetic captions that include these relations. They made a huge dataset called SR4G, which has millions of image-caption pairs for training and thousands more for testing. The goal is to improve current systems by fine-tuning them with the SR4G data. This leads to better results, especially when using simpler models. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
