Semi-supervised salient object detection via joint semantic segmentation


Semi-supervised salient object detection via joint semantic segmentation – This paper describes a simple and efficient method for multi-label learning under high visual appearance variance. To this end, we present an automatic algorithm for segmenting the joint shapes in a 2D object segmentation algorithm. We develop a new technique for segmenting the joint shapes and train the segmentation algorithm using a novel multi-label CNN architecture. To optimize the segmentation, we propose a new CNN architecture, known as the Multi-Rendering Network, that is trained by minimizing the variance in the joint shapes and the cost in both the number of training images and the number of joint shapes. This method achieves high segmentation accuracies on a variety of objects of interest including human, horse, human silhouette, human body part, and human silhouette using a standard image classification framework.

We present a new semantic segmentation framework for semantic segmentation of nouns. Based on deep convolutional neural networks (CNNs), our model is capable of learning to distinguish nouns from other classes. Furthermore, it learns to distinguish nouns across domains, which we call the domain embedding. Our model can effectively embed noun classes as well as classes of verbs into embeddings with a natural representation, in which each sentence is a single word or an adjective with a singular or two-part noun. We evaluate the performance of our model using the UCI 2017 Short-term Memory Challenge.

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Semi-supervised salient object detection via joint semantic segmentation

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    Learning to Compose Verb Classes Across DomainsWe present a new semantic segmentation framework for semantic segmentation of nouns. Based on deep convolutional neural networks (CNNs), our model is capable of learning to distinguish nouns from other classes. Furthermore, it learns to distinguish nouns across domains, which we call the domain embedding. Our model can effectively embed noun classes as well as classes of verbs into embeddings with a natural representation, in which each sentence is a single word or an adjective with a singular or two-part noun. We evaluate the performance of our model using the UCI 2017 Short-term Memory Challenge.


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