of the condition fed in input. This enables a single model to work reliably across diverse conditions without changes at inference time. The same concept also proved effective in rainy conditions and fully-supervised settings. As shown in the Figure, the results are striking. Code, models, and generated images are available here. Additionally, Stefano’s work includes contributions around an end-to-end method for panoptic segmentation, domain generalization techniques using uncertainty estimation and plausible adversarial augmentations, extreme generalization to unseen objects of completely unknown categories, and depth prediction for dynamic objects with a weak radar supervision. From safer autonomous driving to seamless 3D product visualization, Stefano continues pushing the boundaries of computer vision for real-world impact. 25 DAILY WACV Sunday Stefano Gasperini Figure 1. The md4all framework. A frozen daytime - depth model estimates on daytime samples and provides guidance to another model fed with a mix of daytime and nighttime inputs. Inference is done with a simple single model.
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