Welcome to GAIIP

2026 International Conference on Generative Artificial Intelligence and Image Processing

The 2026 International Conference on Generative Artificial Intelligence and Image Processing (GAIIP 2026) will be grandly held in Guangzhou, China, from January 30 to February 1, 2026.

GAIIP 2026 will bring together top scholars, technical experts, and industry representatives from the global field of generative artificial intelligence and image processing to jointly discuss the latest research findings and application developments in generative models, deep generative networks, image synthesis and understanding, multimodal generation, and advanced visual computing technologies.

The conference covers Generative Adversarial Networks (GAN), diffusion models, self-supervised learning technologies, large-scale visual data processing, and intelligent algorithms. It focuses on theoretical innovation and engineering practice, and promotes innovative applications and cross-domain integration in multiple fields such as digital content creation, medical image analysis, virtual reality, and autonomous driving.

The topics of interest for submission include, but are not limited to

	Generative AI New development of generative adversarial networks ( GAN ) Natural language processing and text generation technology AI-driven music generation system Application of Generative AI in Image Synthesis AI-based automatic content generation The interpretability and transparency of the generative AI model The application of deep learning in generative AI Copyright and ethical issues of AI-generated content Adaptive and multimodal generation techniques The application of reinforcement learning in generative AI
	Image processing Image enhancement and denoising Image segmentation and target detection Medical image processing Remote sensing image analysis Image Compression and Coding Image restoration and reconstruction Image style migration Video Analysis and Processing Multimodal image processing Image quality assessment Face recognition and expression analysis
	Machine learning  Broad Learning System Machine learning Deep learning Reinforcement learning Learning Transfer Knowledge graph Path planning Transfer learning Generating adversarial networks Adversarial learning Dual learning Distributed learning Meta-learning
	Artificial intelligence Machine learning Data mining Machine perception Virtual reality Natural language processing Neural network Deep learning