Generating Multimodal Images with GAN: Integrating Text, Image, and Style
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Abstract (arXiv)
In the field of computer vision, multimodal image generation has become a research hotspot, especially the task of integrating text, image, and style. In this study, we propose a multimodal image generation method based on Generative Adversarial Networks (GAN), capable of effectively combining text descriptions, reference images, and style information to generate images that meet multimodal requirements. This method involves the design of a text encoder, an image feature extractor, and a style integration module, ensuring that the generated images maintain high quality in terms of visual content and style consistency. We also introduce multiple loss functions, including adversarial loss, text-image consistency loss, and style matching loss, to optimize the generation process. Experimental results show that our method produces images with high clarity and consistency across multiple public datasets, demonstrating significant performance improvements compared to existing methods. The outcomes of this study provide new insights into multimodal image generation and present broad application prospects.
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References · 12
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- 2024Exploiting Diffusion Prior for Out-of-Distribution Detectioncited by 7
- 2024Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention modelcited by 7
- 2024DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Networkcited by 3
- 2024Irregularity Inspection using Neural Radiance Fieldcited by 2
- 2024Detecting and Classifying Defective Products in Images Using YOLOcited by 2
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