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Found 18 papers, 1 papers with code
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover
Inference on high-order graphical models has become increasingly important in recent years.
Efficient Regularization of Squared Curvature
Curvature has received increased attention as an important alternative to length based regularization in computer vision.
Submodularization for Quadratic Pseudo-Boolean Optimization
We propose a general optimization framework based on local submodular approximations (LSA).
Efficient Squared Curvature
Curvature has received increasing attention as an important alternative to length based regularization in computer vision.
Submodularization for Binary Pairwise Energies
We propose a general optimization framework based on local submodular approximations (LSA).
Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion
Although this is not as good as the factor of 2 approximation of the well known expansion algorithm, we achieve very good results in practice.
Joint Optimization of Segmentation and Color Clustering
We propose to make clustering an integral part of segmentation, by including a new clustering term in the energy function.
A-expansion for multiple "hedgehog" shapes
A single click and +/-90 degrees normal orientation constraints reduce our hedgehog prior to star-convexity.
Hedgehog Shape Priors for Multi-Object Segmentation
In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes.
Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments.
Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies
First, unlike LSA-AUX which selects auxiliary functions based solely on the current solution, we propose to incorporate several additional criteria.
Location Augmentation for CNN
For example, a salient object is more likely to be closer to the center of the image, the sky in the top part of an image, etc.
K-convexity shape priors for segmentation
As shown in the paper, for many forms of convexity our regularization model is significantly more descriptive for any given k. Our shape prior is useful in practice, e. g. in biomedical applications, and its optimization is robust to local minima.
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Previous work develops efficient approximate optimization based on mean field inference, which is a local optimization method and can be far from the optimum.
Simulating CRF with CNN for CNN
After our CRF Simulator is trained, it can be directly incorporated as part of any larger CNN architecture, enabling a seamless end-to-end training.
Sparse Non-Local CRF
We propose a new sparse non-local CRF: it has a sparse number of connections, but it has both local and non-local ones.
Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection
Regularized loss tends to have lower values for the more likely object segments, and thus it can be used to fine-tune an already trained CNN to a given test image, adapting to images unseen during training.
Regularized Loss for Weakly Supervised Single Class Semantic Segmentation
We propose a new weakly supervised method for training CNNs to segment an object of a single class of interest.
