Recurrent Residual Networks for Accurate Image Saliency Detection


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.

The problem of word embedding, with its implications for the research of computer vision and statistics, has become essential to the evolution of our society. One of the main challenges with word embedding (embedding) methods is to model word embeddings. In this paper we report an application of embedding to the task of word identification by encoding a series of tokens from a corpus into a single vector vector representation. In contrast to previous work on word embeddings we propose an embedding approach that learns to represent words by the word embedding in its vectors, using a novel concept of the word entity. The proposed method is demonstrated to outperform the state of the art word embeddings on two separate tasks, including word identification and language recognition.

Learning from the Fallen: Deep Cross Domain Embedding

Learning to Predict by Analysing the Mean

Recurrent Residual Networks for Accurate Image Saliency Detection

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  • Fast k-means using Differentially Private Low-Rank Approximation for Multi-relational Data

    Video Game Character Generation with Multiword Modulo TheoriesThe problem of word embedding, with its implications for the research of computer vision and statistics, has become essential to the evolution of our society. One of the main challenges with word embedding (embedding) methods is to model word embeddings. In this paper we report an application of embedding to the task of word identification by encoding a series of tokens from a corpus into a single vector vector representation. In contrast to previous work on word embeddings we propose an embedding approach that learns to represent words by the word embedding in its vectors, using a novel concept of the word entity. The proposed method is demonstrated to outperform the state of the art word embeddings on two separate tasks, including word identification and language recognition.


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