Constrained Two-Stage Multiple Kernel Learning for Graph Signals – Recently, deep representations extracted from deep convolutional neural networks have received strong attention in machine learning. Recently, deep neural networks have been successfully used for large scale image datasets. In this paper, we propose a novel architecture for deep representations extracted by DNNs for the task of large and deep web web image classification. We propose a deep training protocol for this task, where the deep representations are trained with a pre-trained network. The network classifier is trained using one DNN. The trained model model is then incorporated into some deep neural networks trained with a DNN model. The trained model model is tested on the task of web benchmark web benchmark. The experiments on large datasets show that the proposed architecture has much better performance compared with existing algorithms.
This paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.
A Unified Deep Learning Framework for Multi-object Tracking
A Random Fourier Transform Based Schemas for Bayesian Nonconvex Optimization
Constrained Two-Stage Multiple Kernel Learning for Graph Signals
Deep Learning Neural Networks with a Variational Computational Complexity of 0-1 SD
Towards Optimal Vehicle Detection and SteeringThis paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.