Add to ORKGEven though convolutional neural networks (CNN) has achieved near-human performance in various computer vi-sion tasks, its ability to tolerate scale variations is lim-ited. The popular practise is making the model bigger first, and then train it with data augmentation using ex-tensive scale-jittering. In this paper, we propose a scale-invariant convolutional neural network (SiCNN), a model designed to incorporate multi-scale feature exaction and classification into the network structure. SiCNN uses a multi-column architecture, with each column focusing on a particular scale. Unlike previous multi-column strategies, these columns share the same set of filter parameters by a scale transformation among them. This design deals with scale variat...
Abstract—Recently image recognition becomes vital task using several methods. One of the most intere...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tas...
AbstractConvolutional Neural Networks (CNNs) require large image corpora to be trained on classifica...
While scale-invariant modeling has substantially boosted the performance of visual recognition tasks...
Most works related to convolutional neural networks (CNN) use the traditional CNN framework which ex...
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification fo...
Attention mechanisms are widely used for Convolutional Neural Networks (CNNs) when performing variou...
Developing computational algorithms to model the biological vision system has challenged researchers...
Modelled closely on the feedforward conical structure of the primate vision system - Convolutional N...
We propose a Convolutional Neural Network (CNN), which encodes local scale invariance and equivarian...
We propose a Convolutional Neural Network (CNN), which encodes local scale invariance and equivarian...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
We explore the role of scale for improved feature learning in convolutionalnetworks. We propose mult...
Abstract—Recently image recognition becomes vital task using several methods. One of the most intere...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tas...
AbstractConvolutional Neural Networks (CNNs) require large image corpora to be trained on classifica...
While scale-invariant modeling has substantially boosted the performance of visual recognition tasks...
Most works related to convolutional neural networks (CNN) use the traditional CNN framework which ex...
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification fo...
Attention mechanisms are widely used for Convolutional Neural Networks (CNNs) when performing variou...
Developing computational algorithms to model the biological vision system has challenged researchers...
Modelled closely on the feedforward conical structure of the primate vision system - Convolutional N...
We propose a Convolutional Neural Network (CNN), which encodes local scale invariance and equivarian...
We propose a Convolutional Neural Network (CNN), which encodes local scale invariance and equivarian...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
We explore the role of scale for improved feature learning in convolutionalnetworks. We propose mult...
Abstract—Recently image recognition becomes vital task using several methods. One of the most intere...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...
The ability to handle large scale variations is crucial for many real world visual tasks. A straight...