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Papers on “convolutional neural networks image recognition classification”

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1. Large-Scale Video Classification with Convolutional Neural Networks

   A. Karpathy, G. Toderici, Sanketh Shetty, et al. · 2014 · 2014 IEEE Conference on Computer Vision and Pattern Recognition · 6,731 cites

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2. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

   Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, et al. · 2016 · IEEE transactions on medical imaging · 5,175 cites

   Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer

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3. Siamese Neural Networks for One-Shot Image Recognition

   Gregory Koch, R. Zemel, R. Salakhutdinov · 2015 · 4,839 cites

4. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

   Srdjan Sladojevic, M. Arsenović, A. Anderla, et al. · 2016 · Computational Intelligence and Neuroscience · 1,919 cites

   The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. A

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5. Bag of Tricks for Image Classification with Convolutional Neural Networks

   Tong He, Zhi Zhang, Hang Zhang, et al. · 2018 · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 1,659 cites

   Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstra

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6. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification

   Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, et al. · 2017 · IEEE Transactions on Geoscience and Remote Sensing · 998 cites

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7. Breast cancer histopathological image classification using Convolutional Neural Networks

   F. Spanhol, L. Oliveira, Caroline Petitjean, et al. · 2016 · 2016 International Joint Conference on Neural Networks (IJCNN) · 854 cites

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8. Review of Image Classification Algorithms Based on Convolutional Neural Networks

   Leiyu Chen, Shaobo Li, Q. Bai, et al. · 2021 · Remote. Sens. · 744 cites

   Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field. Convolutional neural networks (CNNs) have gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. In the wake of these successes, CNN-based methods have emerged in remote sensing image scene classification and achieved advanced classification accuracy. In this review, which focuses on the application of

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