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Adaptive Log-Euclidean Metrics for SPD Matrix Learning
Ziheng Chen, Yue Song, Tianyang Xu +3
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode underlying structural correlation in data. Man…
Low-rank features based double transformation matrices learning for image classification
Yu-Hong Cai, Xiao-Jun Wu, Zhe Chen
Linear regression is a supervised method that has been widely used in classification tasks. In order to apply linear regression to classification tasks, a technique for relaxing re…
Differentiable Neural Architecture Learning for Efficient Neural Network Design
Qingbei Guo, Xiao-Jun Wu, Josef Kittler +1
Automated neural network design has received ever-increasing attention with the evolution of deep convolutional neural networks (CNNs), especially involving their deployment on emb…
Self-grouping Convolutional Neural Networks
Qingbei Guo, Xiao-Jun Wu, Josef Kittler +1
Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, mos…
Discriminative Supervised Hashing for Cross-Modal similarity Search
Jun Yu, Xiao-Jun Wu, Josef Kittler
With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search. As multiple modal da…
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
Jun Yu, Xiao-Jun Wu, Josef Kittler
Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have…