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Found 5 papers, 0 papers with code
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation.
Fast and Accurate Image Upscaling With Super-Resolution Forests
The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input.
Accurate Object Detection with Joint Classification-Regression Random Forests
In this way, we can simultaneously predict the object probability of a window in a sliding window approach as well as regress its aspect ratio with a single model.
Alternating Decision Forests
Contrary to Boosted Trees, in our method the loss minimization is an inherent part of the tree growing process, thus allowing to keep the benefits of common Random Forests, such as, parallel processing.
Human Pose Estimation Using Body Parts Dependent Joint Regressors
The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts.
