Papers Superpixel Image Classification
“Superpixel Image Classification” 태그가 달린 논문 9편 · 필터 해제
On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer
Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information. Consequently, for a larger image dataset, CNNs and ViTs face deplo…
Superpixel Image ClassificationIs an Image Also Worth 16x16=256 Superpixels? A Framework for Attentional Image Classification
Superpixel-based image classification has traditionally leveraged graph neural networks (GNNs) for processing irregular image representations. Recent advances in computer vision, driven by Vision Transformers (ViTs), hav…
Superpixel Image ClassificationDomain GeneralizationEscaping the Big Data Paradigm with Compact Transformers
With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to be…
Fine-Grained Image ClassificationImage ClassificationSuperpixel Image ClassificationParameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks
We present a novel graph convolutional layer that is fast, conceptually simple, and provides high accuracy with reduced overfitting. Based on pseudo-differential operators, our layer operates on graphs with relative posi…
image-classificationImage ClassificationPositionSuperpixel Image ClassificationA 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. It is often the case that certain parts of a point cloud are m…
ClusteringGeneral Classificationimage-classificationImage Classification+1Superpixel Image Classification with Graph Attention Networks
This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect ne…
ClassificationGeneral ClassificationGraph AttentionGraph Neural Network+5Graph Convolutional Gaussian Processes
We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning …
BIG-bench Machine LearningGaussian ProcessesSuperpixel Image ClassificationTranslationSplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
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. Our main contribution is a novel convolution ope…
Deep LearningGeneral ClassificationGraph ClassificationNode Classification+1Geometric deep learning on graphs and manifolds using mixture model CNNs
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) …
Deep LearningDocument ClassificationGraph ClassificationGraph Regression+6