58 papers with code • 0 benchmarks • 0 datasets
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Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.
For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme.
As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison.
Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation
While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue.
Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation.
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.