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Text-to-Image Generation

15 papers with code · Computer Vision
Subtask of Image Generation

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StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

19 Oct 2017hanzhanggit/StackGAN

In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.

TEXT-TO-IMAGE GENERATION

StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

ICCV 2017 hanzhanggit/StackGAN

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.

TEXT-TO-IMAGE GENERATION

Generative Adversarial Text to Image Synthesis

17 May 2016hanzhanggit/StackGAN

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.

ADVERSARIAL TEXT TEXT-TO-IMAGE GENERATION

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

CVPR 2018 taoxugit/AttnGAN

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.

#3 best model for Text-to-Image Generation on COCO (SOA-C metric)

TEXT MATCHING TEXT-TO-IMAGE GENERATION

Generating Images from Captions with Attention

9 Nov 2015mansimov/text2image

Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions.

TEXT-TO-IMAGE GENERATION

Provably Robust Blackbox Optimization for Reinforcement Learning

7 Mar 2019FlorianWilk/SpotMicroAI

Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics.

TEXT-TO-IMAGE GENERATION

Generating Multiple Objects at Spatially Distinct Locations

ICLR 2019 tohinz/multiple-objects-gan

Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.

CONDITIONAL IMAGE GENERATION TEXT-TO-IMAGE GENERATION

Controllable Text-to-Image Generation

NeurIPS 2019 mrlibw/ControlGAN

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions.

TEXT-TO-IMAGE GENERATION

Controllable Text-to-Image Generation

NeurIPS 2019 mrlibw/ControlGAN

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions.

TEXT-TO-IMAGE GENERATION

Semantic Object Accuracy for Generative Text-to-Image Synthesis

29 Oct 2019tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis

To address the aforementioned challenges we introduce both a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption.

IMAGE CAPTIONING TEXT-TO-IMAGE GENERATION