Text-Guided Image Manipulation via Generative Adversarial Network With Referring Image Segmentation-Based Guidance
- Resource Type
- Periodical
- Authors
- Watanabe, Y.; Togo, R.; Maeda, K.; Ogawa, T.; Haseyama, M.
- Source
- IEEE Access Access, IEEE. 11:42534-42545 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Image segmentation
Text recognition
Generative adversarial networks
Image color analysis
Visualization
Image reconstruction
Text processing
Text-guided image manipulation
text-to-image synthesis
generative adversarial network
referring image segmentation
- Language
- ISSN
- 2169-3536
This study proposes a novel text-guided image manipulation method that introduces referring image segmentation into a generative adversarial network. The proposed text-guided image manipulation method aims to manipulate images containing multiple objects while preserving text-unrelated regions. The proposed method assigns the task of distinguishing between text-related and unrelated regions in an image to segmentation guidance based on referring image segmentation. With this architecture, the adversarial generative network can focus on generating new attributes according to the text description and reconstructing text-unrelated regions. For the challenging input images with multiple objects, the experimental results demonstrate that the proposed method outperforms conventional methods in terms of image manipulation precision.