RoIAlign:基于backbone输出的image feature和模型输入proposal box,提取出box feature N个dynamic instance interactive head: 在proposal feature上应用self-attention,以推理object之间的relation 对proposal feature和box feature进行interaction,得到最终的object feature 回归层和分类层:基于object feature预测位置并分类 Backbo...
对condition number来个一句话总结:condition number是一个矩阵(或者它所描述的线性系统)的稳定性或者敏感度的度量,如果一个矩阵的condition number在1附近,那么它就是well-conditioned的,如果远大于1,那么它就是ill-conditioned的,如果一个系统是ill-conditioned的,它的输出结果就不要太相信了。 从优化或者数值计算的...
The Supplementary Section 7 describes the manner in which feature block separation is achieved. For the several million edge-wise features, to reduce the large number of network edges and coefficients (NE-time and CO—many of them zeros), we only considered edges between the 24 nodes found ...
Due to the efficient data representation ability of sparse coding, a number of studies have used it to develop feature extraction and representations for human activity recognition. For instance, sparse coding method was presented Zhu, Zhao, Fu, and Liu (2010) to convert feature in activity ...
For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. ...
FeatureCost.m)forthe features% (used when optimizingfors, whichiscalled featureMatrixinthisexercise)%2) sparseCodingWeightCost (insparseCodingWeightCost.m)forthe weights% (used when optimizingforA, whichiscalled weightMatrixinthisexericse)% We reduce the number of features and number of patches...
A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing...
Sparse representation is a powerful technique to perform feature extraction and classification while removing irrelevant interferences (Li et al., 2021; Xing et al., 2021), besides, this technique can acquire a concise representation of the signal. The classical sparsity-inducing term includes ℓ0...
In addition to their excellent performance, these portfolios have only a small number of active positions, a desirable feature for small investors, for whom the fixed overhead portion of the transaction cost is not negligible. JEL Classification: G11, C00 展开 ...
A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem mo... P Dollar,V Rabaud,G Cottrell,... - Joint IEEE International Workshop on Visual Surveillance & Performance Evaluation of Tracking & ...