Dual Adversarial Inference for Text-to-Image Synthesis

Lao, Qicheng; Havaei, Mohammad; Pesaranghader, Ahmad; Dutil, Francis; Jorio, Lisa Di; Fevens, Thomas

doi:10.1109/iccv.2019.00766

Cited by 33 publications

(27 citation statements)

References 14 publications

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“…GAN-based Text-to-image generation. In the past few years, Generative Adversarial Networks (GANs) [18] have shown promising results on text-to-image generation [5,8,9,14,17,22,[27][28][29][30]34,39,40,46,47,54,[56][57][58][62][63][64][65][66][67][68]. GAN-INT-CLS [46] was the first to use a conditional GAN formulation for text-to-image generation.…”

Section: Related Workmentioning

confidence: 99%

Vector Quantized Diffusion Model for Text-to-Image Synthesis

Gu¹,

Chen²,

Bao³

et al. 2021

Preprint

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We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality. The code and models will be available at https: //github.com/microsoft/VQ-Diffusion.

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Section: Related Workmentioning

confidence: 99%

Vector Quantized Diffusion Model for Text-to-Image Synthesis

Gu¹,

Chen²,

Bao³

et al. 2021

Preprint

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“…We group T2I models that take the generated image and pass it through an image captioning [67,68,69] or image encoder network [70], thereby creating a cycle to the input description or latent code, as cycle consistency approaches.…”

Section: Cycle Consistencymentioning

confidence: 99%

Adversarial Text-to-Image Synthesis: A Review

Frolov,

Hinz,

Raue

et al. 2021

Preprint

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With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last years regarding visual realism, diversity, and semantic alignment. However, the field still faces several challenges that require further research efforts such as enabling the generation of high-resolution images with multiple objects, and developing suitable and reliable evaluation metrics that correlate with human judgement. In this review, we contextualize the state of the art of adversarial text-to-image synthesis models, their development since their inception five years ago, and propose a taxonomy based on the level of supervision. We critically examine current strategies to evaluate textto-image synthesis models, highlight shortcomings, and identify new areas of research, ranging from the development of better datasets and evaluation metrics to possible improvements in architectural design and model training. This review complements previous surveys on generative adversarial networks with a focus on text-to-image synthesis which we believe will help researchers to further advance the field.

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“…T2IG performance using the stacked structure [7] as the basic structure is further improved [8], [31] by adding a word-level attention mechanism [9]. Recently, more complex and effective structures for T2IG were proposed [8], [29], [32], [33]. For instance, Qiao et al [8] proposed a model structure similar to the encoder-decoder, called MirrorGAN that tried to recover the text descriptions based on the synthesized images to guarantee semantic consistency between the text description and generated image.…”

Section: A Natural Language-to-image Generationmentioning

confidence: 99%

Generating Images From Spoken Descriptions

Wang

Qiao

Zhu

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Text-based technologies, such as text translation from one language to another, and image captioning, are gaining popularity. However, approximately half of the world's languages are estimated to be lacking a commonly used written form. Consequently, these languages cannot benefit from text-based technologies. This paper presents 1) a new speech technology task, i.e., a speech-to-image generation (S2IG) framework which translates speech descriptions to photo-realistic images 2) without using any text information, thus allowing unwritten languages to potentially benefit from this technology. The proposed speechto-image framework, referred to as S2IGAN, consists of a speech embedding network and a relation-supervised denselystacked generative model. The speech embedding network learns speech embeddings with the supervision of corresponding visual information from images. The relation-supervised denselystacked generative model synthesizes images, conditioned on the speech embeddings produced by the speech embedding network, that are semantically consistent with the corresponding spoken descriptions. Extensive experiments are conducted on four public benchmark databases: two databases that are commonly used in text-to-image generation tasks, i.e., CUB-200 and Oxford-102 for which we created synthesized speech descriptions, and two databases with natural speech descriptions which are often used in the field of cross-modal learning of speech and images, i.e., Flickr8k and Places. Results on these databases demonstrate the effectiveness of the proposed S2IGAN on synthesizing highquality and semantically-consistent images from the speech signal, yielding a good performance and a solid baseline for the S2IG task.

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Dual Adversarial Inference for Text-to-Image Synthesis

Cited by 33 publications

References 14 publications

Vector Quantized Diffusion Model for Text-to-Image Synthesis

Vector Quantized Diffusion Model for Text-to-Image Synthesis

Adversarial Text-to-Image Synthesis: A Review

Generating Images From Spoken Descriptions

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