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   Articles with "convolution network" as a keyword



3D convolution network and Siamese-attention mechanism for expression recognition

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recommendations! Published in 2019 at "Multimedia Tools and Applications"

DOI: 10.1007/s11042-019-07860-2

Abstract: Researches on Expression recognition focus on common subject-independent task, while cross-database evaluation is rare and lack of universal protocol. The key challenge for both tasks is to extract features that effectively describe the pattern of… read more here.

Keywords: convolution network; expression recognition; network; attention mechanism ... See more keywords

End-to-end dilated convolution network for document image semantic segmentation

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recommendations! Published in 2021 at "Journal of Central South University"

DOI: 10.1007/s11771-021-4731-9

Abstract: Semantic segmentation is a crucial step for document understanding. In this paper, an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and programming. To extract semantic structures from… read more here.

Keywords: network; end; dilated convolution; segmentation ... See more keywords

Intelligent fault diagnosis of rotating machinery based on improved hybrid dilated convolution network for unbalanced samples

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-98553-4

Abstract: In practical industrial applications, obtaining a sufficient number fault samples for specific types of equipment fault can be challenging. As a result, there are frequently significantly fewer defect samples obtained than healthy samples, and the… read more here.

Keywords: fault; convolution network; dilated convolution; network ... See more keywords

Lite AU convolution network driven by a small amount of samples

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recommendations! Published in 2017 at "Electronics Letters"

DOI: 10.1049/el.2017.0272

Abstract: A novel lite action unit (AU) convolution network (LAUCN) is proposed for automatic AU detection, which could improve the accuracy of AU detection with a few samples. (i) LAUCN could transform the manual intervened factors… read more here.

Keywords: convolution network; small amount; amount samples;

A hyperspectral image classification method based on feature enhancement and a hybrid deformable convolution network

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recommendations! Published in 2024 at "Remote Sensing Letters"

DOI: 10.1080/2150704x.2024.2311782

Abstract: ABSTRACT In recent years, some hyperspectral image (HSI) classification methods based on deep models have shown excellent performance. Most deep models receive three-dimensional (3D) block structures as input to extract spectral-spatial features from HSI data.… read more here.

Keywords: classification; method; hybrid deformable; convolution network ... See more keywords

Spatial-Temporal Aggregation Graph Convolution Network for Efficient Mobile Cellular Traffic Prediction

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recommendations! Published in 2022 at "IEEE Communications Letters"

DOI: 10.1109/lcomm.2021.3138075

Abstract: Accurate cellular traffic prediction is challenging due to the complex spatial topology of cellular network and the dynamic temporal feature of mobile traffic. To overcome these problems, this letter proposes a spatial-temporal aggregation graph convolution… read more here.

Keywords: spatial temporal; traffic; convolution network; graph convolution ... See more keywords

An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting

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recommendations! Published in 2022 at "IEEE Intelligent Transportation Systems Magazine"

DOI: 10.1109/mits.2019.2962138

Abstract: Traffic forecasting is a challenging problem because of the irregular and complex road network in space and the dynamic and non-stationary traffic flow in time. To solve this problem, the recently proposed temporal graph convolution… read more here.

Keywords: traffic; convolution network; graph convolution; network ... See more keywords

Continuous Volumetric Convolution Network With Self-Learning Kernels for Point Clouds

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recommendations! Published in 2023 at "IEEE Transactions on Consumer Electronics"

DOI: 10.1109/tce.2022.3218107

Abstract: Although Convolutional Neural Networks (CNNs) have achieved large successes on image data, the attributes of point cloud data, such as its irregular format and sparse 3D distribution, prevent CNNs from being applied to point cloud… read more here.

Keywords: point clouds; self learning; convolution network; continuous volumetric ... See more keywords

Dual-Graph Collaboration: Bidirectional Fusion Graph Convolution Network for Structure Multidefect Positioning and Assessment

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recommendations! Published in 2024 at "IEEE Transactions on Industrial Informatics"

DOI: 10.1109/tii.2024.3435441

Abstract: Graph convolution network can extract structural multidefect information well, and has been widely concerned in the field of structural damage detection. However, it is difficult to locate and evaluate defects of different sizes only using… read more here.

Keywords: structure; convolution network; dual graph; graph convolution ... See more keywords

Hybrid Fault Diagnosis of Multiple Open-Circuit Faults for Cascaded H-Bridge Multilevel Converter Based on Perturbation Estimation Convolution Network

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recommendations! Published in 2024 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2024.3351234

Abstract: This article proposes a hybrid fault diagnosis method based on perturbation estimation convolution network (PECN) of multiple open-circuit switch faults for cascaded H-bridge (CHB) multilevel converter. The proposed perturbation observer as the model-based method can… read more here.

Keywords: convolution network; perturbation; perturbation estimation; fault ... See more keywords

Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction

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recommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2022.3208943

Abstract: Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are introduced into traffic prediction and achieve state-of-the-art performance due to their good ability for modeling the spatial and temporal property of traffic data. In… read more here.

Keywords: traffic prediction; convolution network; traffic; graph ... See more keywords