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As a challenging task, text-to-image generation aims to generatephoto-realistic and semantically consistent images according to the given textdescriptions. Existing methods mainly extract the text information from onlyone sentence to represent an image and the text representation effects thequality of the generated image well. However, directly utilizing the limitedinformation in one sentence misses some key attribute descriptions, which arethe crucial factors to describe an image accurately. To alleviate the aboveproblem, we propose an effective text representation method with thecomplements of attribute information. Firstly, we construct an attribute memoryto jointly control the text-to-image generation with sentence input. Secondly,we explore two update mechanisms, sample-aware and sample-joint mechanisms, todynamically optimize a generalized attribute memory. Furthermore, we design anattribute-sentence-joint conditional generator learning scheme to align thefeature embeddings among multiple representations, which promotes thecross-modal network training. Experimental results illustrate that the proposedmethod obtains substantial performance improvements on both the CUB (FID from14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.

Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation