scholarly journals MRDA-MGFSNet: Network Based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1351
Author(s):  
Maopeng Li ◽  
Guoxiong Zhou ◽  
Weiwei Cai ◽  
Jiayong Li ◽  
Mingxuan Li ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Attention Mechanism ◽  
Good Effect ◽  
Complex Environment ◽  
High Background ◽  
Symmetrical Structure ◽  
Fine Grained ◽  
Multi Scale ◽  
Spatial Features

Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Network based on Multi-rate Dilated Attention Mechanism and Multi-granularity Feature Sharer (MRDA-MGFSNet). First, in this network, in order to effectively identify similar patterns between butterflies and suppress the information that is similar to the butterfly’s features in the background but is invalid, a Multi-rate Dilated Attention Mechanism (MRDA) with a symmetrical structure which assigns different weights to channel and spatial features is designed. Second, fusing the multi-scale receptive field module with the depthwise separable convolution module, a Multi-granularity Feature Sharer (MGFS), which can better solve the recognition problem of a small inter-class variance and reduce the increase in parameters caused by multi-scale receptive fields, is proposed. In order to verify the feasibility and effectiveness of the model in a complex environment, compared with the existing methods, our proposed method obtained a mAP of 96.64%, and an F1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies.

scholarly journals Non-Local and Multi-Scale Mechanisms for Image Inpainting

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3281
Author(s):  
Xu He ◽  
Yong Yin
Keyword(s):  
Markov Random Fields ◽  
Receptive Fields ◽  
Image Inpainting ◽  
Long Distance ◽  
Visual Appearance ◽  
Fine Grained ◽  
Multi Scale ◽  
Non Local ◽  
Relationship Of

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

2021 ◽  
pp. 016173462110425
Author(s):  
Jianing Xi ◽  
Jiangang Chen ◽  
Zhao Wang ◽  
Dean Ta ◽  
Bing Lu ◽  
...  
Keyword(s):  
Congenital Anomaly ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Scale Model ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

scholarly journals Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

2020 ◽  
Vol 12 (3) ◽  
pp. 582 ◽  
Author(s):  
Rui Li ◽  
Shunyi Zheng ◽  
Chenxi Duan ◽  
Yang Yang ◽  
Xiqi Wang
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Feature Maps ◽  
Spatial Features ◽  
Training Samples ◽  
Series Of Experiments

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

scholarly journals Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism

2020 ◽  
Vol 11 ◽  
Author(s):  
Guofeng Yang ◽  
Yong He ◽  
Yong Yang ◽  
Beibei Xu
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Attention Mechanism ◽  
Classification Model ◽  
Classification Models ◽  
Fine Grained ◽  
Complex Scenes ◽  
Crop Disease ◽  
Visual Disturbances

Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.

scholarly journals Associating Multi-Scale Receptive Fields For Fine-Grained Recognition

2020 ◽  
Author(s):  
Zihan Ye ◽  
Fuyuan Hu ◽  
Yin Liu ◽  
Zhenping Xia ◽  
Fan Lyu ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Fine Grained ◽  
Multi Scale

Multi-scale Sparse Network with Cross-Attention Mechanism for image-based butterflies fine-grained classification

2022 ◽  
pp. 108419
Author(s):  
Maopeng Li ◽  
Guoxiong Zhou ◽  
Weiwei Cai ◽  
Jiayong Li ◽  
Mingxuan Li ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Sparse Network ◽  
Fine Grained ◽  
Multi Scale

scholarly journals Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

2019 ◽  
Author(s):  
Li Rui ◽  
Zheng Shunyi ◽  
Duan Chenxi ◽  
Yang Yang ◽  
Wang Xiqi
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Empirical Studies ◽  
The State ◽  
Attention Mechanism ◽  
Spatial Features ◽  
Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

scholarly journals Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 114
Author(s):  
Fan Yang ◽  
Deming Yang ◽  
Zhiming He ◽  
Yuanhua Fu ◽  
Kui Jiang
Keyword(s):  
Loss Function ◽  
Smart Cities ◽  
Low Cost ◽  
Detection Algorithm ◽  
Commercial Vehicles ◽  
Generalization Ability ◽  
Fine Grained ◽  
Multi Scale ◽  
Scale Optimization

Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification accuracy of distant cars, we propose a reformed YOLOv3 (You Only Look Once, version 3) algorithm to realize the detection of various types of automobiles, such as SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks and pickup trucks. Based on the dataset UA-DETRAC-LITE, manually labeled data is added to improve the data balance. First, data optimization for the vehicle target is performed to improve the generalization ability and position regression loss function of the model. The experimental results show that, within the range of 67 m, and through scale optimization (i.e., by introducing multi-scale training and anchor clustering), the classification accuracies of trucks and pickup trucks are raised by 26.98% and 16.54%, respectively, and the overall accuracy is increased by 8%. Secondly, label smoothing and mixup optimization is also performed to improve the generalization ability of the model. Compared with the original YOLO algorithm, the accuracy of the proposed algorithm is improved by 16.01%. By combining the optimization of the position regression loss function of GIOU (Generalized Intersection Over Union), the overall system accuracy can reach 92.7%, which improves the performance by 21.28% compared with the original YOLOv3 algorithm.

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