Generative Adversarial Network Including Referring Image Segmentation For Text-Guided Image Manipulation
Yuto Watanabe, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
This paper proposes a novel generative adversarial network to improve the performance of image manipulation using natural language descriptions that contain desired attributes. Text-guided image manipulation aims to semantically manipulate an image aligned with the text description while preserving text-irrelevant regions. To achieve this, we newly introduce referring image segmentation into the generative adversarial network for image manipulation. The referring image segmentation aims to generate a segmentation mask that extracts the text-relevant region. By utilizing the feature map of the segmentation mask in the network, the proposed method explicitly distinguishes the text-relevant and irrelevant regions and has the following two contributions. First, our model can pay attention only to the text-relevant region and manipulate the region aligned with the text description. Second, our model can achieve an appropriate balance between the generation of accurate attributes in the text-relevant region and the reconstruction in the text-irrelevant regions. Experimental results show that the proposed method can significantly improve the performance of image manipulation.