MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

Chen, Bingyu; Xia, Min; Huang, Junqing

doi:10.3390/rs13040731

Cited by 71 publications

(31 citation statements)

References 43 publications

(52 reference statements)

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“…Under the assumption that both local data and local models are available, a probabilistic FL framework is developed and studied, with special emphasis on training and aggregation neural network models. Estimated local model parameters (in the case of a neural network, a set of weight vectors) between data sources are matched to build a global network [43,44]. When the data are available, the method is proposed by training the local model for each data source in parallel.…”

Section: Federated Bayesian Networkmentioning

confidence: 99%

Federated Learning: A Distributed Shared Machine Learning Method

Xia

et al. 2021

Complexity

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Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning.

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Section: Federated Bayesian Networkmentioning

confidence: 99%

Federated Learning: A Distributed Shared Machine Learning Method

Xia

et al. 2021

Complexity

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“…In this way, not only the original backbone network feature information and the hidden layer feature information extracted from the original SAFT module are retained, but also the backbone network feature map containing the spatial semantic information of the hidden layer is added. Through the GFR module, different types of feature images can be fused [35], which can help to further improve the segmentation accuracy. The calculation and derivation process of GFR is shown in Equation ( 8):…”

Section: Global Feature Refinement Modulementioning

confidence: 99%

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Ding

Weng

Xia

et al. 2021

IJGI

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Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

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“…To further improve the detection performance of detecting objects in difficult conditions based multiscale features, multilayer proposal [8] applied a deconvolution module to a lightweight architecture to generate enhanced feature map which can improve small object detection. MFANet [9] proposed a multilevel feature aggregation network which first extracts the deep features and filters the redundant channel information to optimize the learned context and then uses high-level features to provide guidance information for low-level features. In DAU-Net [10], deep feature information is fused with shallow feature information through multiscale attention modules to improve to the accuracy of water segmentation.…”

Section: Deep Cnn Detection Framework Based On Multilayermentioning

confidence: 99%

Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection

Nguyen

2021

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Vehicle detection is a crucial task in autonomous driving systems. Due to large variance of scales and heavy occlusion of vehicle in an image, this task is still a challenging problem. Recent vehicle detection methods typically exploit feature pyramid to detect vehicles at different scales. However, the drawbacks in the design prevent the multiscale features from being completely exploited. This paper introduces a feature pyramid architecture to address this problem. In the proposed architecture, an improving region proposal network is designed to generate intermediate feature maps which are then used to add more discriminative representations to feature maps generated by the backbone network, as well as improving the computational cost of the network. To generate more discriminative feature representations, this paper introduces multilayer enhancement module to reweight feature representations of feature maps generated by the backbone network to increase the discrimination of foreground objects and background regions in each feature map. In addition, an adaptive RoI pooling module is proposed to pool features from all pyramid levels for each proposal and fuse them for the detection network. Experimental results on the KITTI vehicle detection benchmark and the PASCAL VOC 2007 car dataset show that the proposed approach obtains better detection performance compared with recent methods on vehicle detection.

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MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

Cited by 71 publications

References 43 publications

Federated Learning: A Distributed Shared Machine Learning Method

Federated Learning: A Distributed Shared Machine Learning Method

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection

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