Text-To-Image Generation Has The Answer To Everything

Despite its widespread usage, text-to-image generation is still a problem, mainly due to the lack of suitable and scalable methods. In this paper, we present a brief overview of the current status of text-to-image generation, describe some of the problems that have been addressed, and present experiments with low-resource language text-image datasets. We also evaluate two widely used text-to-image synthesis algorithms, StackGAN++ and X-GAN, in terms of their performance on such datasets. The results demonstrate that text-to-image generation is a complex task and that there is a need for scalable methods.

Abstract

During the last couple of years, researchers have made considerable progress in text-to-image generation. Recent techniques can generate photo-realistic images based on arbitrary texts. However, the ability to generate realistic, believable natural scenes is still a challenge.

The latest GAN-based text-to-image generation techniques can match the description’s semantic content and produce photo-realistic images. This is especially true when the text in question has the same meaning in different languages. Nevertheless, multilingual text-to-image generation models face challenges such as language imbalance.

Several approaches have been proposed for overcoming these challenges. Some of them rely on fine-grained word embedding to refine text features. Others incorporate local visual information to enhance image quality. The best performer in our opinion, is the attentional generative adversarial network (AttnGAN) model. The model was trained on Google captions data set and compared with other methods on several benchmarks.

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The novelty of the model is the fact that it exploits natural language descriptions to generate images. This can be done in a number of ways, such as by re-composing text features in stages, or by taking advantage of semantic knowledge to select suitable masks. This is a promising way to achieve the ‘best-of-both-worlds’ effect, as natural language descriptions are not usually associated with the real world.

Another intriguing approach is the use of a one-sided label smoothing scheme. This alleviates overconfidence problems and improves training stability. It also imposes an additional penalty if the discriminator correctly classifies the text.

The previous methods mainly adopt a stack generator-discriminator pair. This may be not easy to implement for image-only datasets. In contrast, self-supervised learning is a good choice for text-to-image synthesis.

Problems with text-to-image synthesis

Various frameworks have been proposed to handle local visual information. The main challenge in text-to-image synthesis is generation of photo-realistic images from text descriptions. In general, the goal of text-to-image synthesis is to generate a plausible image for a caption. In other words, the image should be semantically and visually consistent with the description. However, linguistic discrepancy often leads to deviating generated images. To avoid this, a novel conditional generative model is envisioned to generate images with both clarity and semantic relevance.

One of the most challenging tasks in text-to-image synthesis is to correctly generate images that have the right colors, shapes and textures. This is made difficult by the fact that the pixel-level contents of the images are a lot higher dimensional than the high-level concepts of the text descriptions. For this reason, a more effective pre-training strategy is necessary. In addition to improving the quality of the synthetic images, this approach will also allow for more granular control over the visual output of the generator.

The main drawback is the need for a large number of training examples. In addition, the most recent approaches are built on the foundation of generative adversarial networks, a technology with inherent challenges. Luckily, a few clever algorithms have been developed to address the problem. In particular, the deep attentional multimodal similarity model has been employed to learn the fine-grained image-text representation. The model uses a combination of L1 distance functions and feature matching to achieve this feat. The algorithm can be thought of as a two-stage process: the first stage encoding the features from the input text and the second stage generating the final image.

StackGAN++

StackGAN++ is a generative adversarial network that generates photo-realistic fine-grained images conditioned on semantic texts. It achieves high performance in both CUB and Oxford-102 benchmarks. It is a three-stage generative adversarial network with an integrated network structure. It achieves the best Inception Score (IS) on the CUB dataset. It outperforms the other GANs by 0.23. It has successfully synthesized images with vivid eyes and clear backgrounds.

In a text-to-image task, the GAN needs to learn the exact mapping from semantic text distribution to visual image distribution. It must also learn to discriminate fake generated images from real ground-truth images. This requires additional conditioning variables.

Recent GANs have difficulty in explicitly discriminating between foreground and background images. They typically produce realistic images for almost half of the captions. However, the quality of the images is not stable in most cases. They also cannot identify problems in sentences with multiple objects.

This work proposes an extension of the StackGAN++ architecture. It utilizes local features to generate more realistic images and proposes an attention mechanism. The model associates the subregions of the resulting image with the words that are most relevant in the input text. This enables the model to recognize semantic context better.

In addition to the aforementioned features, the network also uses a leaky-ReLU compression method to compress the encoded text description. It then concatenates the description to a noise vector z. The encoded report is sampled in Generator G, which is optimized for image realism. The generated image is then validated against the input texts.

In order to generate a higher-resolution image, the model uses content information to create an image at different scales for the same scene. The generated image is aligned with the bounding box.

Results from experiments on low-resource language text-image datasets

Getting a Handwritten Text Recognition system up and running on a budget is no small feat. This is especially true for languages with a small number of alphabets and a small number of phonemes. Luckily, there are tools and tricks that can help. One such trick involves the use of a feature map. Basically, a feature map is a set of features that are propagated along a backbone. Specifically, the feature map is a set of two feature maps – the support and the query feature maps. The query features are derived from the support ones via a clever augmentation scheme.

The result is a multilingual text-image system with a near 100% success rate. In addition, the system possesses a suite of features that is highly configurable. These include feature map configurations, alphabets and a selection of phonemes. A set of training sets is also available, allowing for the creation of bespoke feature maps. Lastly, the system has a user interface to configure the features. The user can then proceed to a decoding algorithm to perform the required final transcription. This is achieved with the assistance of a nifty little attention mechanism. The attention mechanism uses depth-wise cross-correlating of the query and support feature maps. The feature map is augmented with a handful of samples from the input set. This is done in a controlled environment, ensuring that no data is lost in the process. It also ensures that the feature map is not over-used and the system is as efficient as it can be.

Summary

Compared with textual description, visual representation of information is more expressive. However, abstract concepts are not easily encoded via images. Hence, image-text translation aims to create a redescription with the same semantics as the original description. Using a multi-modal summary can help users to distill the most important information and gain deeper insights.

Text-to-image generation methods have made considerable progress in creating realistic images. Moreover, these methods have also enabled the synthesis of rich and unique images. Despite these achievements, effective and consistent text descriptions still remain a challenge. Luckily, there is a new approach that can be used to overcome these challenges. eDiff-I is a novel text-to-image diffusion model that guarantees high correlation between a text prompt and its generated image.

The eDiff-I method takes text as input, and it uses a series of expert denoisers to guide style transfer from the input prompt text. It also generates a new sentence to guide the creation of a personalized creation. It also generates a corresponding image with all details of the text description. The generated images appear convincing and are surprisingly realistic.

This model is composed of a recurrent neural network to embed the text, a two-level discriminator to enhance the image quality, and a fast R-CNN based object-wise discriminator to preserve the semantics. Its performance is compared with that of state-of-the-art methods. The results show that the eDiff-I method performs better than the baseline method at all settings.

The GLAM framework also leverages local attention and global attention to improve semantic consistency. It generates target images from coarse to fine scales. It also uses a multi-stage cascaded generator to produce images.