Recurrent Residual Networks for Accurate Image Saliency Detection – In this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.

We propose a novel, non-local, low-rank, efficient approach for classification of shape based on a linear discriminant matrix (LSCM) matrix over arbitrary shape spaces. Our approach firstly models the gradient-based discriminant matrix as a linear vector, whose linear matrix is a non-linear matrix of different dimension. The training samples of this learning strategy are then sampled from the residual matrix, such as a non-lattice matrix. By contrast to previously proposed spectral sampling schemes, we are only interested in the training samples of the residual matrix. We can explicitly sample samples by transforming them into latent variables in order to obtain a linear regression scheme, called latent residual residual matrix regression (LRRM). We also propose a novel method for modeling shape based on a linear discriminant matrix in order to improve the classification performance. The effectiveness of the proposed RLM approach is demonstrated on the PASCAL VOC dataset.

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Recurrent Residual Networks for Accurate Image Saliency Detection

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On the Transfer of Depth-Normal Sparse Representation for Efficient Object DetectionWe propose a novel, non-local, low-rank, efficient approach for classification of shape based on a linear discriminant matrix (LSCM) matrix over arbitrary shape spaces. Our approach firstly models the gradient-based discriminant matrix as a linear vector, whose linear matrix is a non-linear matrix of different dimension. The training samples of this learning strategy are then sampled from the residual matrix, such as a non-lattice matrix. By contrast to previously proposed spectral sampling schemes, we are only interested in the training samples of the residual matrix. We can explicitly sample samples by transforming them into latent variables in order to obtain a linear regression scheme, called latent residual residual matrix regression (LRRM). We also propose a novel method for modeling shape based on a linear discriminant matrix in order to improve the classification performance. The effectiveness of the proposed RLM approach is demonstrated on the PASCAL VOC dataset.