In this paper, we introduce a novel task, termed as semi-supervised domain generalization, to study how to interact the labeled and unlabeled domains, and establish two benchmarks including a web-crawled dataset, which poses a novel yet realistic challenge to push the limits of existing technologies.
Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications.
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
This paper proposes a homotopy coordinate descent (HCD) method to solve the $l_0$-norm regularized least square ($l_0$-LS) problem for compressed sensing, which combine the homotopy technique with a variant of coordinate descent method.
After optimized, a subset of optimal features will be selected in group, and the number of selected features will be determined automatically.
Feature selection is an important data pre-processing in data mining and machine learning, which can reduce feature size without deteriorating model's performance.
We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles.
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
To our best knowledge, this is the first attempt to analyze individual extraversion of crowd motions based on trajectories.
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.