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Found 21 papers, 5 papers with code
WorDepth: Variational Language Prior for Monocular Depth Estimation
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints.
Monitored Distillation for Positive Congruent Depth Completion
In the absence of ground truth for model selection and training, our method, termed Monitored Distillation, allows a student to exploit a blind ensemble of teachers by selectively learning from predictions that best minimize the reconstruction error for a given image.
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.
An Adaptive Framework for Learning Unsupervised Depth Completion
We present a method to infer a dense depth map from a color image and associated sparse depth measurements.
Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion
Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs.
Unsupervised Segmentation Incorporating Shape Prior via Generative Adversarial Networks
We present an image segmentation algorithm that is developed in an unsupervised deep learning framework.
Regularization in network optimization via trimmed stochastic gradient descent with noisy label
The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm.
Stochastic batch size for adaptive regularization in deep network optimization
We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework.
Adaptive Regularization via Residual Smoothing in Deep Learning Optimization
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework.
Adaptive Weight Decay for Deep Neural Networks
Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model.
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth.
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks
We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent.
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces
We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions.
Adaptive Regularization of Some Inverse Problems in Image Analysis
We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems.
Multi-Label Segmentation via Residual-Driven Adaptive Regularization
We present a variational multi-label segmentation algorithm based on a robust Huber loss for both the data and the regularizer, minimized within a convex optimization framework.
Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems
The desired properties, robustness and effectiveness, of the regularization parameter selection in a variational framework for imaging problems are achieved by merely replacing the static regularization parameter with our adaptive one.
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions.
FAST LABEL: Easy and Efficient Solution of Joint Multi-Label and Estimation Problems
We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition.
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
We illustrate the use of this motion estimation scheme in propagating a segmentation across frames and show that it leads to more accurate segmentation than traditional motion estimation that does not make use of physical constraints.
A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems
The advantage of this statistical model is that the underlying variables: the labels and the functions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates.
