Superpixel Image Classification
5 papers with code • 1 benchmarks • 2 datasets
A Superpixel Image classification can be classified the group of pixels that share common characteristics (like pixel intensity ) or segementize the common pixel value in to one group.
Benchmarks
These leaderboards are used to track progress in Superpixel Image Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MNIST
MPI FAUST Dataset
Most implemented papers
Escaping the Big Data Paradigm with Compact Transformers
SHI-Labs/Compact-Transformers
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Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
rusty1s/pytorch_geometric
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CVPR 2018
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Superpixel Image Classification with Graph Attention Networks
This paper presents a methodology for image classification using Graph Neural Network (GNN) models.
A Dynamic Reduction Network for Point Clouds
Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries.
