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Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied...
Object detection aims to identify instances of semantic objects of a certain class in images or videos. The success of state-of-the-art approaches is attributed to the significant progress of object proposal and convolutional neural networks (CNNs). Most promising detectors involve multi-task learning with an optimization objective of softmax loss and regression loss. The first is for multi-class...
Selecting local features is crucial in generating robust compact descriptors for mobile visual search. The state-of-the-art MPEG Compact Descriptors for Visual Search (CDVS) standard has utilized the intrinsic characteristics (e.g., scale, orientation, peak, center distance, etc.) of interest points to select salient local features for selective aggregation and compression of local feature descriptors...
In this paper, a group-sensitive multiple kernel learning (GS-MKL) method is proposed for object recognition to accommodate the intraclass diversity and the interclass correlation. By introducing the “group” between the object category and individual images as an intermediate representation, GS-MKL attempts to learn group-sensitive multikernel combinations together with the associated classifier....
In research work, we found that grammatical information in the Modern Chinese Grammar Information Dictionary is very effective to revise chunk border. So the Modern Chinese Grammar Information Dictionary used to extract the chunk Border Revised Rules (BRR). In this paper, a new method of chunking is proposed--combined with BRR and TBL, SVM used for chunking. We reduced the number of SVM feature vector,...
The complex Interconnections between markers and polygenic genotype value suggested that the regression was not enough for describing the relation between genes and traits. Artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems. Recently, artificial neural networks had been successfully used to predict the polygenic genotype value, and the different learning...
Using the measured frequency response function (FRF) data, a novel algorithm for structural health monitoring is proposed based on the principal component analysis (PCA) in this paper. First, the measured FRF data for a structure in both the healthy and the damaged states are used as the initial data. A PCA transformation is performed to obtain the features of an intact structure, in which an orthogonal...
In the past, a prediction equation based on the single nucleotide polymorphisms (SNP) is derived to calculate genomic breeding values (GEBV). However, the genome is very complex; a function could not reflect the relation between markers and phenotypes. Unlike the methods of regression, artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems, however, artificial...
Although linear multivariate approaches used to analyze large genetic data sets did not allow a large part of the total variance to be explained, strong distortions with nonlinear data sets, horseshoe effects had always been found. Artificial neural networks could gather their knowledge by detecting the patterns and relationships in data and learn through experience, and could perform well for optimization...
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