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Found 6 papers, 1 papers with code
Continuous U-Net: Faster, Greater and Noiseless
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
Shadows in videos are difficult to detect because of the large shadow deformation between frames.
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Contrastive Registration for Unsupervised Medical Image Segmentation
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.
