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Adversarially Tuned Scene Generation
Although simulated data augmented with a few real world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, and intrinsic images etc.).
Model Validation for Vision Systems via Graphics Simulation
We adapt the methodology in the context of current graphics simulation tools for modeling data generation processes and, for systematic performance characterization and trade-off analysis for vision system design leading to qualitative and quantitative insights.
Simulations for Validation of Vision Systems
As the computer vision matures into a systems science and engineering discipline, there is a trend in leveraging latest advances in computer graphics simulations for performance evaluation, learning, and inference.
