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Found 4 papers, 0 papers with code
WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions.
WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather
To this end, we create the WeatherProof Dataset, the first semantic segmentation dataset with accurate clear and adverse weather image pairs, which not only enables our new training paradigm, but also improves the evaluation of the performance gap between clear and degraded segmentation.
Enhancing Diffusion Models with 3D Perspective Geometry Constraints
We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images.
WeatherStream: Light Transport Automation of Single Image Deweathering
We introduce a pipeline that uses the power of light-transport physics and a model trained on a small, initial seed dataset to reject approximately 99. 6% of unwanted scenes.
