  Generating images according to natural language descriptions is a challenging
task. In this work, we propose the Combined Attention Generative Adversarial
Network (CAGAN) to generate photo-realistic images according to textual
descriptions. The proposed CAGAN utilises two attention models: word attention
to draw different sub-regions conditioned on related words; and
squeeze-and-excitation attention to capture non-linear interaction among
channels. With spectral normalisation to stabilise training, our proposed CAGAN
improves the state of the art on the IS and FID on the CUB dataset and the FID
on the more challenging COCO dataset. Furthermore, we demonstrate that judging
a model by a single evaluation metric can be misleading by developing an
additional model adding local self-attention which scores a higher IS,
outperforming the state of the art on the CUB dataset, but generates
unrealistic images through feature repetition.
