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...
详细信息
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 ...
详细信息
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.
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.
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...
详细信息
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.
The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However...
详细信息
Different from the content-based image retrieval methods, cross-modal image retrieval methods uncover the rich semantic-level information of social images to further understand image contents. As multiple modal data d...
详细信息
Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anom...
详细信息
Instance segmentation plays an important role in image *** Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instanc...
详细信息
Instance segmentation plays an important role in image *** Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial *** solve these problems,a real-time instance segmentation algorithm based on contour learning was ***,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution ***,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more ***,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real ***,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance *** experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti *** proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.
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...
详细信息
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.
Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative repr...
详细信息
暂无评论