Conventional subspace learning approaches based on image gradient orientations only employ the first-order gradient information. However, recent researches on human vision system (HVS) uncover that the neural image is...
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This paper presents an effective method that can detect fabric defects. The method utilizes the optimal Gabor filter and binary random drift particle swarm algorithm (BRDPSO) that can implement feature selection and p...
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The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learning compositional embedding have shown ...
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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.
An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the pr...
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ISBN:
(纸本)9781665446006
An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the process of training the Siamese Neural Network, hard negative pairs are selective to achieve fast convergence and good performance. The DCNN learning features are combined with the unique shape feature to enable accurate and efficient retrieval of massive image data. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency.
We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities including pixel-le...
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The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data *** aiming at the problem that it cannot be appli...
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The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data *** aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this *** new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization *** new algorithm is compared with some popular multi-view clustering *** effectiveness of the new algorithm is proved through the analysis of the experimental results.
In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is pro...
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In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image ***,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection ***,semantema of each image feature map is enhanced by semantic information from all subsequent feature ***,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of ***,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target *** experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,*** the AP by 8.78%and 5.49%on medium and hard difficulty ***,our method achieves greater detection performance compared with many mainstream multi-modal methods,*** the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.
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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The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data seeks to provide a more comprehensive characterization of target objects. Multimodal data possess distinct semantic st...
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The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data seeks to provide a more comprehensive characterization of target objects. Multimodal data possess distinct semantic structures in both spectral and spatial dimensions, making efficient feature complementarity and redundancy elimination crucial. To this end, we propose a self-distillation-based multimodal feature alignment network (DFANet), which employs two branches to capture spectral and spatial similarities, respectively, and integrates structural discriminative information from LiDAR at two stages for more effective multimodal data integration. The network comprises three main components: a feature alignment fusion module (FAFM), an offset attention module (OAM), and a self-distillation mechanism. Specifically, the FAFM guides feature alignment through channel-assimilative mapping of multimodal data. The OAM addresses boundary patch classification challenges by learning offset weights of reference points. The self-distillation mechanism filters out irrelevant information during feature alignment by enhancing the coordination between high-level and low-level features. Adequate experiments indicate that our method achieves better results compared to the most recent hyperspectral classification methods on three public datasets.
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