Text-to-Image Generation is a task in computer vision and natural language processing where the goal is to generate an image that corresponds to a given textual description. This involves converting the text input into a meaningful representation, such as a feature vector, and then using this representation to generate an image that matches the description.
image-generation
Text-to-Image Generation
3260 papers • 126 benchmarks • 313 datasets
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in image-generation
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MS COCO
MS COCO
CUB
CUB
Multi-Modal-CelebA-HQ
Multi-Modal-CelebA-HQ
Libraries
iUse these libraries to find image-generation models and implementations
4 papers 144
Datasets
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Subtasks
Most implemented papers
Show and tell: A neural image caption generator
O. Vinyals, Samy Bengio, D. Erhan, Alexander Toshev•Sun Nov 16 2014
This paper presents a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.
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