Text-to-image generation, i.e. generating an image given a text description, is a very challenging task due to the significant semantic gap between the two domains. Humans, however, tackle this problem intelligently. We learn from diverse objects to form a solid prior about semantics, textures, colors, shapes, and layouts. Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details. In this paper, and inspired by this process, we propose a novel text-to-image method called LeicaGAN to combine the above three phases in a unified framework.

Quality The paper is thorough in describing the method and supporting the proposed method with experiments Clarity The paper is well written and easy to follow. Originality & Significance Although the method is not very novel in light of Paper 1253: RecreateGAN (see more below), the experimental exploration of different settings of the method is thoroughly done and interesting. The idea of matching local image features to word-level embeddings and matching global image features to sentence level embeddings is intuitive and makes sense. This paper shares significant parts of the method with Paper 1253: RecreateGAN, in particular, the textual-visual embedding loss in (6) of this paper matches the pairwise loss defined in eq (5) of the other paper. However, this paper uses this component as part of a different method, namely for textual-visual embedding vs an image similarity embedding. Additionally, the cascade of attentional generators in this papers is very similar between both papers.

This paper proposes a new method for text to image generation. Pros • A new method is proposed. Cons • There are several issues pointed out by the reviewers. Some of them have been addressed in the author response. The authors have promised to add more.

Text-to-image generation, i.e. generating an image given a text description, is a very challenging task due to the significant semantic gap between the two domains. Humans, however, tackle this problem intelligently. We learn from diverse objects to form a solid prior about semantics, textures, colors, shapes, and layouts. Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details. In this paper, and inspired by this process, we propose a novel text-to-image method called LeicaGAN to combine the above three phases in a unified framework.

Text-to-image generation, i.e. generating an image given a text description, is a very challenging task due to the significant semantic gap between the two domains. Humans, however, tackle this problem intelligently. We learn from diverse objects to form a solid prior about semantics, textures, colors, shapes, and layouts. Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details. In this paper, and inspired by this process, we propose a novel text-to-image method called LeicaGAN to combine the above three phases in a unified framework.