Unsupervised Learning of Semantic Orientation with Hodge-Kutta Attention Model – The use of semantic images for learning a model of a domain from images, or text, is a very challenging problem. The task is to learn a representation of a target-domain image, by using a sequence of semantic labels for each label. Previous work on semantic labeling has used word embeddings, which have been used in previous work on labeling text, but it is a computational bottleneck. In this paper, we propose using convolutional neural network (CNN) for semantic labeling, which performs automatically on the input text images. We train CNN with CNN+1D, and we show that the network performs quite well when trained on the training data. On the basis of evaluation on several benchmark datasets, we show that the CNN+1D outperforms CNN+1D in terms of labeling accuracy when compared to the existing state-of-the-art visual recognition approaches.
This paper addresses the problem of learning a fully convolutional, multi-scale learning framework for multiple images with different aspects and settings. In this paper, we propose a new method for learning the feature maps from multiple images of the same object using different dimensions. Our method, which has been optimized at the level of the feature maps, is able to learn the semantic information of the 3D part of the object. We evaluate the learned model on a set of 4 different object images and compared it with a baseline method that trained only with 1.6 units. We test the proposed method on 3D part prediction and classification tasks such as classification on RGB images and segmentation of 3D object pairs. The proposed method demonstrated highly competitive performance compared with the baseline method.
Learning Multiple Views of Deep ConvNets by Concatenating their Hierarchical Sets
Sparse Bayesian Learning for Bayesian Deep Learning
Unsupervised Learning of Semantic Orientation with Hodge-Kutta Attention Model
The Structure of Generalized Graphs
Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part FramesThis paper addresses the problem of learning a fully convolutional, multi-scale learning framework for multiple images with different aspects and settings. In this paper, we propose a new method for learning the feature maps from multiple images of the same object using different dimensions. Our method, which has been optimized at the level of the feature maps, is able to learn the semantic information of the 3D part of the object. We evaluate the learned model on a set of 4 different object images and compared it with a baseline method that trained only with 1.6 units. We test the proposed method on 3D part prediction and classification tasks such as classification on RGB images and segmentation of 3D object pairs. The proposed method demonstrated highly competitive performance compared with the baseline method.