Although DALL-E has shown an impressive ability of composition-based
systematic generalization in image generation, it requires the dataset of
text-image pairs and the compositionality is provided by the text. In contrast,
object-centric representation models like the Slot Attention model learn
composable representations without the text prompt. However, unlike DALL-E its
ability to systematically generalize for zero-shot generation is significantly
limited. In this paper, we propose a simple but novel slot-based autoencoding
architecture, called SLATE, for combining the best of both worlds: learning
object-centric representations that allows systematic generalization in
zero-shot image generation without text. As such, this model can also be seen
as an illiterate DALL-E model. Unlike the pixel-mixture decoders of existing
object-centric representation models, we propose to use the Image GPT decoder
conditioned on the slots for capturing complex interactions among the slots and
pixels. In experiments, we show that this simple and easy-to-implement
architecture not requiring a text prompt achieves significant improvement in
in-distribution and out-of-distribution (zero-shot) image generation and
qualitatively comparable or better slot-attention structure than the models
based on mixture decoders.

本文介绍了一种结合了 DALL-E 的构成性和 Slot Attention 模型的目标 - 中心表示模型的自动编码架构，名为 SLATE，可以在没有文本的情况下进行系统化的泛化，该模型基于 Image GPT 解码器，可以用于处理图像，实验结果表明，相较于混合解码器模型，该模型在内态和外态图像生成方面有着显著提高，并且具有与混合解码器模型相当或更好的感知机制结构。