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Found 9 papers, 4 papers with code
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.
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.
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data
With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth for training.
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion.
Unsupervised Image Restoration Using Partially Linear Denoisers
The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications.
Multi-task deep learning for image segmentation using recursive approximation tasks
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
A multi-task U-net for segmentation with lazy labels
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
Learning to segment microscopy images with lazy labels
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
