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Automatically generate data unit tests that actually catch the data errors that matter for your specific downstream tasks.
Seemingly innocuous choices in loss functions and training regimes can significantly hinder visual geometry estimation, even for state-of-the-art methods.
Generative training not only enhances a model's ability to manipulate objects in images, but also surprisingly strengthens its spatial reasoning skills.
A single model now rivals specialized vision-language models in understanding, while also generating and editing images, thanks to a unified discrete diffusion framework.
