面对网络图像的爆炸性增长,网络图像标注成为近年来一个热点研究内容,稀疏特征选择在提升网络图像标注效率和性能方面发挥着重要的作用.提出了一种增强稀疏性特征选择算法,即,基于l2,1/2矩阵范数和共享子空间的半监督稀疏特征选择算法(s...面对网络图像的爆炸性增长,网络图像标注成为近年来一个热点研究内容,稀疏特征选择在提升网络图像标注效率和性能方面发挥着重要的作用.提出了一种增强稀疏性特征选择算法,即,基于l2,1/2矩阵范数和共享子空间的半监督稀疏特征选择算法(semi-supervised sparse feature selection based on l2,1/2-matix norm with shared subspace learning,简称SFSLS)进行网络图像标注.在SFSLS算法中,应用l2,1/2矩阵范数来选取最稀疏和最具判别性的特征,通过共享子空间学习,考虑不同特征之间的关联信息.另外,基于图拉普拉斯的半监督学习,使SFSLS算法同时利用了有标签数据和无标签数据.设计了一种有效的迭代算法来最优化目标函数.SFSLS算法与其他稀疏特征选择算法在两个大规模网络图像数据库上进行了比较,结果表明,SFSLS算法更适合于大规模网络图像的标注.展开更多
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 detection.I...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 detection.Inspired by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional Transformer.We first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud processing.Then 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 voxel.Lastly,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object detection.The 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 extractor.Experiments results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.展开更多
Let R be a commutative ring with identity, Nn(R) the matrix algebra consisting of all n × n strictly upper triangular matrices over R with the usual product operation. An R-linear map φ : Nn(R) → Nn(R) is said ...Let R be a commutative ring with identity, Nn(R) the matrix algebra consisting of all n × n strictly upper triangular matrices over R with the usual product operation. An R-linear map φ : Nn(R) → Nn(R) is said to be an SZ-derivation of Nn(R) if x2 = 0 implies that φ(x)x+xφ(x) = 0. It is said to be an S-derivation of Nn(R) if φ(x2) = φ(x)x+xφ(x) for any x ∈ Nn(R). It is said to be a PZ-derivation of Nn(R) if xy = 0 implies that φ(x)y+xφ(y) = 0. In this paper, by constructing several types of standard SZ-derivations of Nn(R), we first characterize all SZ-derivations of Nn(R). Then, as its application, we determine all S-derivations and PZ- derivations of Nn(R), respectively.展开更多
文摘面对网络图像的爆炸性增长,网络图像标注成为近年来一个热点研究内容,稀疏特征选择在提升网络图像标注效率和性能方面发挥着重要的作用.提出了一种增强稀疏性特征选择算法,即,基于l2,1/2矩阵范数和共享子空间的半监督稀疏特征选择算法(semi-supervised sparse feature selection based on l2,1/2-matix norm with shared subspace learning,简称SFSLS)进行网络图像标注.在SFSLS算法中,应用l2,1/2矩阵范数来选取最稀疏和最具判别性的特征,通过共享子空间学习,考虑不同特征之间的关联信息.另外,基于图拉普拉斯的半监督学习,使SFSLS算法同时利用了有标签数据和无标签数据.设计了一种有效的迭代算法来最优化目标函数.SFSLS算法与其他稀疏特征选择算法在两个大规模网络图像数据库上进行了比较,结果表明,SFSLS算法更适合于大规模网络图像的标注.
基金National Natural Science Foundation of China(No.61806006)Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781)University Superior Discipline Construction Project of Jiangsu Province。
文摘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 detection.Inspired by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional Transformer.We first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud processing.Then 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 voxel.Lastly,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object detection.The 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 extractor.Experiments results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.
基金Fond of China University of Mining and Technology
文摘Let R be a commutative ring with identity, Nn(R) the matrix algebra consisting of all n × n strictly upper triangular matrices over R with the usual product operation. An R-linear map φ : Nn(R) → Nn(R) is said to be an SZ-derivation of Nn(R) if x2 = 0 implies that φ(x)x+xφ(x) = 0. It is said to be an S-derivation of Nn(R) if φ(x2) = φ(x)x+xφ(x) for any x ∈ Nn(R). It is said to be a PZ-derivation of Nn(R) if xy = 0 implies that φ(x)y+xφ(y) = 0. In this paper, by constructing several types of standard SZ-derivations of Nn(R), we first characterize all SZ-derivations of Nn(R). Then, as its application, we determine all S-derivations and PZ- derivations of Nn(R), respectively.
