Fusion-based hyperspectral image (HSI) superresolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Such a HSI super-resolutio...
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This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existin...
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Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely *** reason maybe that the single label of the data canno...
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Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely *** reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in *** distribution learning contains more information and is a possible way to solve this *** apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is *** the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label ***,in training part,single label learning is used to collect the mean value of the overall distribution of ***,the true value of data label is approached gradually on the granularity of data ***,the whole network model is retrained using the generated label distribution *** results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.
Collaborative representation-based classification (CRC) has demonstrated remarkable progress in the past few years because of its closed-form analytical solutions. However, the existing CRC methods are incapable of pr...
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Distantly supervised relation extraction (DSRE) aims to identify the relation between the two entities (e.g. name and location). Most existing methods extract semantic features from each level separately, without taki...
Distantly supervised relation extraction (DSRE) aims to identify the relation between the two entities (e.g. name and location). Most existing methods extract semantic features from each level separately, without taking into account the transfer of hierarchical knowledge obtained at various levels. As a result, a large amount of knowledge that can improve the quality of the feature representations is lost, resulting in decreased performance for predicting entity relations. In this paper, we propose a novel framework termed the Hierarchical Knowledge Transfer Network (HKTN) that is capable of transferring hierarchical knowledge learned from different levels to improve the performance of predicting entity relations. Specifically, the two representation refinement blocks with re-calibrators at the bag and group levels construct robust bag features and comprehensive group features, respectively. During the construction process, the high-level features are capable of guiding the learning of the bottom-level features using the two re-calibrators. As the construction of the high-level feature representations is based on the bottom-level feature representations, prediction-based contrastive learning fully excavates bottom-level features, which can improve the quality of the feature representation at each level. The experimental results demonstrate that our proposed HKTN achieves an obvious improvement on the two benchmark datasets, including NYT-10 and GDS.
We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently p...
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In recent years, convolutional neural networks have achieved more outstanding results and been widely used in the field of image quality assessment compared with the traditional handcraft method. This paper presents a...
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ISBN:
(纸本)9781538656396
In recent years, convolutional neural networks have achieved more outstanding results and been widely used in the field of image quality assessment compared with the traditional handcraft method. This paper presents a no-reference image quality assessment method based on dual-channel convolutional neural network. The raw image is labeled by visual information fidelity and divided into multiple patches as input. After that, feature extraction is performed by two network channels with different pooling layers. The features are Iinearly stitched and sent to the fully connected layer. The experimental results on the LIVE database and the TID2008 database show that our model has the state-of-the-art performance and obtain a better consistency with human subjective assessment.
The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object *** by the ...
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The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object *** by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional *** first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud *** we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the ***,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object *** voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature *** results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.
Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification. In this paper, we present an efficient structured dictionary learning (ESDL) method which t...
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In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition. Compared to the traditional 2DPCA algorithm, 2DR1-PCA and 2DL1-PCA are based on the R1norm and L1norm, respectively. The advan...
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