Generative Models for Computer Vision
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 244
Special Issue Editors
Interests: computer vision; machine learning
Interests: computer vision; machine learning
Interests: pattern recognition; human–computer interaction; affective computing; computer vision; multi-sensor fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in generative visual modeling have led to a surge in new techniques and methodologies, mainly in the areas of adversarial, auto-regressive, and diffusion models. These approaches have enabled the synthesis of photorealistic images, including ones that are three-dimensionally consistent, and even the generation of entire 3D scenes. Moreover, the ability of such generative models to capture the distribution of given data has been shown to be useful for a wide variety of discriminative visual tasks.
The goal of this Special Issue is to present current advances in generative visual modelling. Its scope will include (but is not limited to) the following areas of research:
- Advances in generative image models;
- Generative models for 3D shape and 3D scene synthesis;
- Benchmarking of generative image models;
- Render-and-compare approaches for visual recognition;
- Self-supervised learning with generative models;
- Out-of-distribution generalization with generative models
We look forward to receiving your valuable contributions.
Dr. Simon Korman
Dr. Dan Rosenbaum
Prof. Dr. Antonio Fernández-Caballero
Guest Editors
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Keywords
- generative models
- realistic image synthesis
- benchmarking image generation
- visual recognition
- self-supervised learning
- out-of-distribution generalization
- three-dimensional scene generation
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