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Generating images according to natural language descriptions is a challengingtask. Prior research has mainly focused to enhance the quality of generation byinvestigating the use of spatial attention and/or textual attention therebyneglecting the relationship between channels. In this work, we propose theCombined Attention Generative Adversarial Network (CAGAN) to generatephoto-realistic images according to textual descriptions. The proposed CAGANutilises two attention models: word attention to draw different sub-regionsconditioned on related words; and squeeze-and-excitation attention to capturenon-linear interaction among channels. With spectral normalisation to stabilisetraining, our proposed CAGAN improves the state of the art on the IS and FID onthe CUB dataset and the FID on the more challenging COCO dataset. Furthermore,we demonstrate that judging a model by a single evaluation metric can bemisleading by developing an additional model adding local self-attention whichscores a higher IS, outperforming the state of the art on the CUB dataset, butgenerates unrealistic images through feature repetition.

CAGAN: Text-To-Image Generation with Combined Attention GANs