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Semantically Consistent Hierarchical Text to Fashion Image Synthesis with an Enhanced-Attentional Generative Adversarial Network
2019
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
In this paper, we present the enhanced Attentional Generative Adversarial Network (e-AttnGAN) with improved training stability for text-to-image synthesis. e-AttnGAN's integrated attention module utilizes both sentence and word context features and performs feature-wise linear modulation (FiLM) to fuse visual and natural language representations. In addition to multimodal similarity learning for text and image features of AttnGAN [28], cosine and feature matching losses of real and generated
doi:10.1109/iccvw.2019.00379
dblp:conf/iccvw/AkLTK19
fatcat:cit4iochkzezrg6lyb6mgaky5i