Multi-Dimensional Gaussian Process Classification – We propose a new deep neural networks-based approach for classifying a target class using a sequence of training samples. Based on two variants of the CNN model, namely, convolution neural networks (CNN) and deep-network-based networks (DNNs), the CNN model is able to classify the samples based on their spatial ordering and temporal ordering. The CNN is a two-layer CNN, which takes its input data points as input and outputs the corresponding prediction results. DNN is a two-layer CNN which can be trained jointly with conventional CNNs. The CNN can predict the classification accuracy with the two-layer CNN, and both the CNN and the deep-network CNN have a representation of the target classes. A preliminary analysis conducted on the UCF101 dataset reported that the CNN model achieves an accuracy of 89.7% which is superior than the conventional CNN model with a baseline of 91.6% and a baseline of 97.8%.
This paper presents a neural-learning approach to object segmentation, which aims at achieving good object recognition performance under various challenging conditions. The model is based on a convolutional Neural Network (CNN), an effective multi-stage architecture and a robust convolutional neural network that is able to capture both feature level information and semantic information. We present a novel representation approach for object segmentation which leverages the recent advances in multi-stage CNN for object segmentation. We evaluate the efficacy of our approach on a set of benchmark datasets.
A Deep RNN for Non-Visual Tracking
Affective Attention Using a Generative Model with Partitioning
Multi-Dimensional Gaussian Process Classification
Probabilistic Neural Encoder with Decision Support for Supervised ClassificationThis paper presents a neural-learning approach to object segmentation, which aims at achieving good object recognition performance under various challenging conditions. The model is based on a convolutional Neural Network (CNN), an effective multi-stage architecture and a robust convolutional neural network that is able to capture both feature level information and semantic information. We present a novel representation approach for object segmentation which leverages the recent advances in multi-stage CNN for object segmentation. We evaluate the efficacy of our approach on a set of benchmark datasets.