Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding


Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding – In this paper, we propose a simple and flexible framework of convolutional neural network (CNN) models that exploit local attention. The model constructs a representation from a set of local features in an iterative process, which are then utilized to reconstruct the target feature representation for the whole network. The main problem arising in CNNs is to estimate an attention vector for each object, while ignoring any attention between them. To overcome this problem we propose a neural network model based on a multi-scale attention mechanism. This model employs features from the local features to learn global attention, which maps each multi-scale attention vector to an attention matrix. The model can generate object representations for the target feature representation, which are used to enhance semantic representations generated by the system. We have conducted extensive experiments on an image-by-image retrieval task. The model demonstrates remarkable performance on the task of image retrieval, outperforming the previous state of the art on all the test datasets.

We consider a probabilistic framework for the task of lexical content prediction. While previous work used the word-level context of the word or a collection of words to predict word-level word embeddings, this work builds on the concept of a word-level context that provides different types of contextual information into the problem. We present a new framework, called word-level context for lexical content prediction. This technique can be used to model the lexical content that is being predicted from a given context and, even without using any word-level context, we can improve the performance of the task.

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Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding

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    A Semantics of TextWe consider a probabilistic framework for the task of lexical content prediction. While previous work used the word-level context of the word or a collection of words to predict word-level word embeddings, this work builds on the concept of a word-level context that provides different types of contextual information into the problem. We present a new framework, called word-level context for lexical content prediction. This technique can be used to model the lexical content that is being predicted from a given context and, even without using any word-level context, we can improve the performance of the task.


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