A Step Toward More Inclusive People Annotations for Fairness

In this article, we will explore the current state-of-the-art techniques for text-to-image generation, which can create images based on textual descriptions. We will focus on three key approaches: (1) using transformers to generate images from text, (2) combining optical flow and depth sensors for video stylization, and (3) leveraging large language models for image synthesis.
Firstly, we will examine the use of transformers for high-resolution image synthesis, which involves predicting images from a combination of text, optical flow, and depth sensors. This approach is similar to machine translation using large language models, where only the structure and text are provided as a prefix to a language model.
Secondly, we will discuss video stylization, where an approach inspired by [15, 23, 82] is used to predict videos from the combination of text, optical flow, and depth sensors. Unlike diffusion-based approaches that use external attention networks or latent blending for stylization, our approach is more closely related to machine translation using large language models in that we only need to provide the structure and text as a prefix to a language model.
Lastly, we will explore multi-axis vision transformers, which are inspired by [62] and can perform video stylization by providing the structure and text as a prefix to a language model. These transformers are more efficient than traditional diffusion-based approaches and can generate high-quality images.
In summary, this article provides an overview of the current state-of-the-art techniques for text-to-image generation, including using transformers, combining optical flow and depth sensors, and leveraging large language models. These approaches have shown promising results in generating high-quality images based on textual descriptions, and they have the potential to revolutionize various industries such as entertainment, advertising, and healthcare.