Generative AI for Vision: A Comprehensive Study of Frameworks and Applications

2025-01-29

AI Summary

This paper addresses key deployment bottlenecks hindering practical adoption of generative AI for image synthesis—namely, high computational overhead, data bias, and poor alignment with user intent. To tackle these challenges, we propose a structured, input-modality–centric taxonomy that unifies modeling across GANs, diffusion models, and conditional generation paradigms. We systematically categorize core tasks—including image-to-image translation, text-to-image generation, domain adaptation, and multimodal alignment—and conduct an in-depth analysis of architectural design principles and applicability boundaries of representative models such as DALL·E, ControlNet, and DeepSeek Janus-Pro. Furthermore, we establish an industrial-deployment–oriented evaluation framework, explicitly delineating optimization pathways for computational efficiency, bias mitigation, and intent alignment. The resulting methodology provides researchers and practitioners with a theoretically grounded yet practically actionable guide for developing and deploying robust, equitable, and controllable generative image systems.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Generative AI is transforming image synthesis, enabling the creation of high-quality, diverse, and photorealistic visuals across industries like design, media, healthcare, and autonomous systems. Advances in techniques such as image-to-image translation, text-to-image generation, domain transfer, and multimodal alignment have broadened the scope of automated visual content creation, supporting a wide spectrum of applications. These advancements are driven by models like Generative Adversarial Networks (GANs), conditional frameworks, and diffusion-based approaches such as Stable Diffusion. This work presents a structured classification of image generation techniques based on the nature of the input, organizing methods by input modalities like noisy vectors, latent representations, and conditional inputs. We explore the principles behind these models, highlight key frameworks including DALL-E, ControlNet, and DeepSeek Janus-Pro, and address challenges such as computational costs, data biases, and output alignment with user intent. By offering this input-centric perspective, this study bridges technical depth with practical insights, providing researchers and practitioners with a comprehensive resource to harness generative AI for real-world applications.

Problem

• Artificial Intelligence
• Image Generation
• Computational Cost

Innovation

• AI Image Generation
• GANs and Stable Diffusion
• Design and Application Framework