Recent advances on generative models for semantic segmentation: a survey

Manish Bhurtel; Danda B. Rawat; Daniel O. Rice

doi:10.1117/12.3014235

7 June 2024 Recent advances on generative models for semantic segmentation: a survey

Manish Bhurtel, Danda B. Rawat, Daniel O. Rice

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 1305113 (2024) https://doi.org/10.1117/12.3014235
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In recent years, computer vision research has witnessed transformative changes with the integration of generative artificial intelligence (AI) models. The generative models have been widely researched in the field of semantic segmentation. In this survey paper, we present a comprehensive review of the generative models, with a specific focus on Generative Adversarial Networks (GANs), Diffusion Models (DMs), and Variational Autoencoders (VAEs), in the realm of semantic segmentation. We incorporate these generative models for model training, image synthesis, semantic label synthesis, image-label pair synthesis, domain adaptation, feature learning, and boundary localization for semantic segmentation. We also perform a thorough comparative analysis highlighting the approach, task, datasets involved, strengths, and weaknesses of the GANs, DMs, and VAEs-based semantic segmentation models. Our comparative evaluation showed a wide range of research works carried out in the generative semantic segmentation domain. This survey consists of diverse generative methodologies, serving as a comprehensive resource for researchers and enthusiasts contributing to the field of generative semantic segmentation.

Conference Presentation

Citation Download Citation

Manish Bhurtel, Danda B. Rawat, and Daniel O. Rice "Recent advances on generative models for semantic segmentation: a survey", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 1305113 (7 June 2024); https://doi.org/10.1117/12.3014235

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