41 datasets found

Pooling is a simple but widely used layer in modern deep CNN architectures for feature aggregation and extraction. Typical CNN design focuses on the conv layers and activation...

The proposed method alternates classification and introspection. The introspection phase employs a strategy to select a sub-window for the next step by applying a beam-search to...

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ConvNeXt.

The dataset used in this paper is a convolutional neural network (CNN) model, specifically ResNet-34, and its layers 20 and 28.

Hyperparameter optimization for transfer Convolutional Neural Networks (CNN) using Genetic Algorithm

Human pose estimation refers to the task of recognizing postures by localizing body keypoints (head, shoulders, elbows, wrists, knees, ankles, etc.) from images.

Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition...

The proposed framework generates a unique set of context-dependent filters based on the input image, and combines them with max-pooling to produce transformation-invariant...

Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new...

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution...

The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category.

Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing.

The dataset used in the paper is CIFAR100 and ImageNet.

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting...

Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels...

ConvMLP: a Hierarchical Convolutional MLP backbone for visual recognition

The dataset used in this paper is a collection of convolutional neural network (CNN) weights, which are compressed using transform quantization.

Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data...

Convolutional Neural Network (CNN) has demonstrated its success in plentiful computer vision application, but typically accompanies high computation cost and numerous redundant...

This dataset is used to train a Convolutional Neural Network (CNN) to classify objects from web images. The dataset is created by downloading images from the web using a query...