Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge

By Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao. 2019

104 citations recorded by the source corpus.

Subfields: Generative Models, Multimodal Learning

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Citations in the corpus

Vector Quantized Diffusion Model for Text-to-Image Synthesis (2021, background): “In the past few years, Generative Adversarial Networks (GANs) [18] have shown promising results on many tasks [19–21],, especially text-to-image generation [5, 8, 9, 14, 17, 25, 27, 31–34, 38, 43, 44, 50, 51, 58, 60–63, 67–73].”
Improving Text-to-Image Synthesis Using Contrastive Learning (2021, methodology): “Recently, a great number of studies [1, 4, 6, 12, 13, 14, 17, 18, 19, 22, 23, 24, 25, 32, 33, 36, 42, 43, 44, 45, 46, 47, 48] present promising results on the text-to-image synthesis task, most of which make use of GANs as the backbone model.”
Cross-Modal Contrastive Learning for Text-to-Image Generation (2021, background): “GAN-based models in particular have shown better sample quality [61, 64, 58, 66, 59, 26, 52, 42, 24].”
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis (2019, methodology): “LeicaGAN [31] adopts text-visual co-embeddings to replace input text with corresponding visual features.”
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (2020, result): “Although impressive results have been presented by previous works [9,19,21,32,33,51,60], there still remain three problems.”
DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis (2020, background): “…its practical applications, text-to-image synthesis has become an active research area recently [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], M. Tao, S. Wu and F. Wu are with the School of Automation, Nanjing University of Posts and Telecommunications,…”
RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge (2020, background): “[19] propose a LeicaGAN to learn and imagine the prior of the diverse objects about semantics, textures, colors, shapes, and layouts.”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “…about it, which is related to image and video captioning [179]; 2) a keyword to the assistant for writing a poem [236]; 3) a piece of text description or a sketch to the assistant for creating an image [180], [237]; 4) lyrics and score to the assistant for composing a piece of music [238].”
Semantic Object Accuracy for Generative Text-to-Image Synthesis (2019, methodology): “[24] decompose the text-to-image process into three distinct phases by first learning a prior over the text-image space, then sampling from this prior, and lastly using the prior to generate the image.”
Blended Diffusion for Text-driven Editing of Natural Images (2021, background): “Recent works that leverage the tremendous progress in vision-language models and datadriven image generation have demonstrated that text-based interfaces for image creation and manipulation are now finally within reach [12, 23, 29, 30, 38, 39, 41, 47, 51, 57].”
Benchmark for Compositional Text-to-Image Synthesis (2021, background): “Other approaches explored introducing memory mechanisms [63, 24], cycle-consistency constraint [37, 17], siamese networks [57, 47], and prior knowledge [36, 4] to improve the text-to-image synthesis performance.”
Adversarial Text-to-Image Synthesis: A Review (2021, background): “LeicaGAN [113] has a multiple priors learning phase in which a text-image encoder learns semantic, texture, and color priors, while a text-mask encoder learns shape and layout priors.”
CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis (2020, methodology): “LeicaGAN [31] adopts text-visual co-embeddings to replace input text with corresponding visual features.”
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (2022, background): “There also exists work on text-to-image synthesis [14, 16, 19, 24, 26, 33, 34, 48, 49, 52, 55, 64, 71].”
Prompt-to-Prompt Image Editing with Cross Attention Control (2022, background): “Numerous works [11, 16, 42, 25, 26, 30, 31, 34, 49, 9, 13, 36] significantly advanced the generation of images conditioned on plain text, known as text-to-image synthesis.”