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The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
[article]
2014
arXiv
pre-print
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In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. ...
Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). ...
In the context of fine-grained classification, finding foreground object and object parts can be regarded as a two-level attention processes, one at object-level and another at part-level. ...
arXiv:1411.6447v1
fatcat:33okswdifzfldmj6pqwn6f4vxu
The application of two-level attention models in deep convolutional neural network for fine-grained image classification
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we propose to apply visual attention to finegrained classification task using deep neural network. ...
Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). ...
We are actively pursuing the above directions.
Acknowledgment ...
doi:10.1109/cvpr.2015.7298685
dblp:conf/cvpr/XiaoXYZPZ15
fatcat:o7bnt3iz5nc5lh5k5gl7okxjrq
Plant Leaf Diseases Fine-Grained Categorization Using Convolutional Neural Networks
2022
IEEE Access
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This paper proposes a fine-grained disease categorization method based on attention network to solve the problem. ...
In "Classification Model", attention mechanism is used to increase identification ability. ...
Aiming at the deficiency of deep neural network in crop disease identification, a fine-grained disease identification method based on attentional deep neural network was proposed for peach and tomato disease ...
doi:10.1109/access.2022.3167513
fatcat:l6nxyhqopfagpfrxutrwqxw6mu
Visual Recognition Based on Deep Learning for Navigation Mark Classification
2020
IEEE Access
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A fine-grained classification model named RMA (ResNet-Multiscale-Attention) based on deep learning is proposed to analyse the subtle and local differences among navigation mark types for the recognition ...
In the RMA model, an attention mechanism based on the fusion of feature maps with three scales is proposed to locate attention regions and capture discriminative characters that are important to distinguish ...
Xiao et al. proposed he two-level attention models in deep convolutional neural network for finegrained image classification, which focus on two different levels of features, object level and part level ...
doi:10.1109/access.2020.2973856
fatcat:x3kzhbnndzgefha5ixqwv5nkte
Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy
2021
Entropy
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With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. ...
At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution ...
The unique characteristics of fine-grained classification make it difficult for traditional convolutional neural network models to effectively identify fine-grained classified images. ...
doi:10.3390/e23070816
fatcat:w4j42gnytvcuxgmhbm3xhexppe
Arabic Text to Image Generation based on Generative Network of Fine-Grained Visual Descriptions
2020
Benha Journal of Applied Sciences
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The important term in our Network is a word level fine-grained image-text matching loss computed by the Deep Attentional Multimodal Similarity Model (DAMSM). ...
With a modern attentional generative network, the Attentional model enable to synthesize fine-grained details at different sub-regions of the image by paying attentions to the related words in the natural ...
In practice, the pre-processed descriptions are in R 1024 .
3.Deep Attentional Multimodal Similarity Model The DAMSM learns two main neural networks that map sub-regions of the image and words of the ...
doi:10.21608/bjas.2020.226901
fatcat:ju2bjtzg2fgovafii5uklda4oa
Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model
2018
Applied Sciences
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The experimental results confirm the high generalization ability of our model in fine-grained image classification. ...
We propose a model based on a triple network and bilinear methods for fine-grained bird identification. ...
The authors of [9] detected the object levels and local areas in fine-grained images using a region-based convolutional neural network (R-CNN) algorithm. ...
doi:10.3390/app8101906
fatcat:aujspgumknbblksvlilha5zgum
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
[article]
2018
arXiv
pre-print
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Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification. ...
RNN-HA consists of three mutually coupled modules: the first module generates image representations for vehicle images, the second hierarchical module models the aforementioned hierarchical dependent relationship ...
Deep neural networks Deep convolutional neural networks (DCNNs) try to model the high-level abstractions of the visual data by using architectures composed of multiple non-linear transformations. ...
arXiv:1812.04239v1
fatcat:suophtgl7vexxhnnhmsw6befeu
A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests
2022
Computational Intelligence and Neuroscience
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models and is more suitable for fine-grained identification applications in complex scenes. ...
Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases ...
Although existing coarse-grained methods have achieved some applications in the meta-level identification of plant pests and diseases, they lack the adequate perception for fine-grained features in the ...
doi:10.1155/2022/4391491
pmid:35665281
pmcid:PMC9162821
fatcat:2344c2ov3jb6pjf6jcobwk5fli
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification
[article]
2018
arXiv
pre-print
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In this work we investigate the suitability of several recent landmark convolutional neural network (CNN) architectures, which have shown top results on large scale image classification tasks, for the ...
Fine-grained vehicle classification is the task of classifying make, model, and year of a vehicle. ...
Their GoogLeNet model was able to surpass the performance of some of the traditional, part-based approaches, reinforcing the belief that using deep convolutional neural networks for fine-grained classification ...
arXiv:1806.02987v1
fatcat:eqqt2fvq5zfffcybigm2mjxyqy
A New Benchmark for Instance-Level Image Classification
2020
IEEE Access
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At the same time, we provide a Simple Classification Head (SCH) technique for the classification of aircraft carriers, with classical convolutional neural network models as the backbone network. ...
Although fine-grained image classification is able to classify more fine-grained sub-categories compared to its coarse-grained counterpart, it often fails to identify individual instances. ...
ACKNOWLEDGMENT The authors would like to thank TopEdit (www.topeditsci. com) for the English language editing of this manuscript. ...
doi:10.1109/access.2020.2986771
fatcat:kc25oudjgjh25gxlrsw5qydtha
Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification
2018
EAI Endorsed Transactions on Scalable Information Systems
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Attention mechanism is widely used in fine-grained image classification. ...
Therefore, this paper proposes a multi-channel attention fusion mechanism based on the deep neural network model which can be trained end-to-end. ...
The author greatly appreciates the anonymous comments of the reviewers. ...
doi:10.4108/eai.27-1-2022.173165
fatcat:oz7zedbvzjgbfodkjcqax76cru
Weakly Supervised Fine-Grained Image Classification via Salient Region Localization and Different Layer Feature Fusion
2020
Applied Sciences
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We tested and verified our model on public datasets released specifically for fine-grained image classification. ...
Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. ...
of Guangdong Joint Fund under Grant U1701262, the Guangdong R&D plan projects in key areas under Grant 2019B010153002, the Guangdong R&D plan projects in key areas under Grant 2018B010109007, the "Blue ...
doi:10.3390/app10134652
fatcat:hjuqmoqydjbphcc7ygc7vfo36e
REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing
[article]
2019
arXiv
pre-print
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Fine-grained image recognition has been a hot research topic in computer vision due to its various applications. ...
Finally, we combine the region attending network and the part sequence learning network into a unified framework that can be trained end-to-end with only image-level labels. ...
Part Sequence Modeling Let X ∈ R (H×W ×C) denote the deep representation through the deep convolutional neural network (the backbone network in Fig. 3) , where H, W and C respectively refer to the height ...
arXiv:1908.01962v1
fatcat:ldt7wg4y2ffelfyyxbyulpugha
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)
2020
Frontiers in Genetics
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Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. ...
In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. ...
Our fine-grained classification method provides more detailed information of histopathological FIGURE 1 | Flow graph of the proposed method of Bilinear Convolutional Neural Networks (BCNNs). ...
doi:10.3389/fgene.2020.547327
pmid:33101377
pmcid:PMC7500315
fatcat:mii32twm2japhkfpodunfpcchy
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