Search Results for author:
Found 14 papers, 2 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.
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
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.
pCON: Polarimetric Coordinate Networks for Neural Scene Representations
Neural scene representations have achieved great success in parameterizing and reconstructing images, but current state of the art models are not optimized with the preservation of physical quantities in mind.
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.
Depth Estimation From Camera Image and mmWave Radar Point Cloud
To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map.
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
A common misconception is that minority inclusion does not increase performance for majority groups alone.
Not Just Streaks: Towards Ground Truth for Single Image Deraining
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image.
Synthetic Generation of Face Videos With Plethysmograph Physiology
Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are beginning to offer a viable path toward non-contact physiological measurement.
Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e. g., heart rate) using facial videos.
Visual Physics: Discovering Physical Laws from Videos
In this paper, we teach a machine to discover the laws of physics from video streams.
Blending Diverse Physical Priors with Neural Networks
Such generalization would require an architecture that can adapt to variations in the correctness of the physics, or in the quality of training data.
Deep Shape from Polarization
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning.
