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Found 10 papers, 3 papers with code
Token Pooling in Vision Transformers
Token Pooling is a simple and effective operator that can benefit many architectures.
Robust Trust Region for Weakly Supervised Segmentation
Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled.
Confluent Vessel Trees with Accurate Bifurcations
We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible.
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component.
Divergence Prior and Vessel-tree Reconstruction
We propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence.
Beyond Gradient Descent for Regularized Segmentation Losses
Both loss functions and architectures are often explicitly tuned to be amenable to this basic local optimization.
Kernel clustering: density biases and solutions
We call it Breiman's bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity.
Secrets of GrabCut and Kernel K-Means
Our bound formulation for kernel K-means allows to combine general pair-wise feature clustering methods with image grid regularization using graph cuts, similarly to standard color model fitting techniques for segmentation.
Kernel Cuts: MRF meets Kernel & Spectral Clustering
We propose a new segmentation model combining common regularization energies, e. g. Markov Random Field (MRF) potentials, and standard pairwise clustering criteria like Normalized Cut (NC), average association (AA), etc.
Thin Structure Estimation with Curvature Regularization
Many applications in vision require estimation of thin structures such as boundary edges, surfaces, roads, blood vessels, neurons, etc.
