Heterogeneous Convolutional Neural Networks for Visual Recognition

2016 ◽  
pp. 262-274 ◽  
Author(s):  
Xiangyang Li ◽  
Luis Herranz ◽  
Shuqiang Jiang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Recognition

scholarly journals Pruning Convolutional Neural Networks with an Attention Mechanism for Remote Sensing Image Classification

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1209 ◽  
Author(s):  
Shuo Zhang ◽  
Gengshen Wu ◽  
Junhua Gu ◽  
Jungong Han
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Recognition ◽  
Remote Sensing Image ◽  
Attention Mechanism ◽  
Aerial Image ◽  
Great Success ◽  
Maximum Information ◽  
Deep Networks

Despite the great success of Convolutional Neural Networks (CNNs) in various visual recognition tasks, the high computational and storage costs of such deep networks impede their deployments in real-time remote sensing tasks. To this end, considerable attention has been given to the filter pruning techniques, which enable slimming deep networks with acceptable performance drops and thus implementing them on the remote sensing devices. In this paper, we propose a new scheme, termed Pruning Filter with Attention Mechanism (PFAM), to compress and accelerate traditional CNNs. In particular, a novel correlation-based filter pruning criterion, which explores the long-range dependencies among filters via an attention module, is employed to select the to-be-pruned filters. Distinct from previous methods, the less correlated filters are first pruned after the pruning stage in the current training epoch, and they are reconstructed and updated during the next training epoch. Doing so allows manipulating input data with the maximum information preserved when executing the original training strategy such that the compressed network model can be obtained without the need for the pretrained model. The proposed method is evaluated on three public remote sensing image datasets, and the experimental results demonstrate its superiority, compared to state-of-the-art baselines. Specifically, PFAM achieves a 0.67% accuracy improvement with a 40% model-size reduction on the Aerial Image Dataset (AID) dataset, which is impressive.

scholarly journals Review of Image Classification Algorithms Based on Convolutional Neural Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 4712
Author(s):  
Leiyu Chen ◽  
Shaobo Li ◽  
Qiang Bai ◽  
Jing Yang ◽  
Sanlong Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Visual Recognition ◽  
Network Models ◽  
Semantic Segmentation ◽  
Basic Structure ◽  
Research Direction ◽  
Object Localization

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 CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures. Along the way, we analyze (1) the basic structure of artificial neural networks (ANNs) and the basic network layers of CNNs, (2) the classic predecessor network models, (3) the recent SOAT network algorithms, (4) comprehensive comparison of various image classification methods mentioned in this article. Finally, we have also summarized the main analysis and discussion in this article, as well as introduce some of the current trends.

scholarly journals A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks

2021 ◽  
Author(s):  
Hannes Vietz ◽  
Tristan Rauch ◽  
Andreas Locklin ◽  
Nasser Jazdi ◽  
Michael Weyrich
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Recognition
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