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.

In this article, we propose a new recurrent neural network architecture for the semantic segmentation task. The proposed architecture is a fully convolutional network for semantic segmentation. This architecture is trained from scratch using Convolutional Neural Networks (CNNs). The performance of the recurrent network of the proposed architecture is evaluated using PASCAL VOC 2015 and the results show that the proposed architecture reduces the visual segmentation time by 50% with no loss in segmentation speed and by a 20% loss in accuracy compared to the traditional Convolutional Neural Network-based solutions. Overall, the proposed architecture yields about 30% improvement over the state-of-the-art results in terms of segmentation speed compared to the state-of-the-art CNN models.

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Semi-supervised salient object detection via joint semantic segmentation

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  • Learning a Latent Polarity Coherent Polarity Model

    Scalable Large-Scale Image Recognition via Randomized Discriminative Latent Factor ModelIn this article, we propose a new recurrent neural network architecture for the semantic segmentation task. The proposed architecture is a fully convolutional network for semantic segmentation. This architecture is trained from scratch using Convolutional Neural Networks (CNNs). The performance of the recurrent network of the proposed architecture is evaluated using PASCAL VOC 2015 and the results show that the proposed architecture reduces the visual segmentation time by 50% with no loss in segmentation speed and by a 20% loss in accuracy compared to the traditional Convolutional Neural Network-based solutions. Overall, the proposed architecture yields about 30% improvement over the state-of-the-art results in terms of segmentation speed compared to the state-of-the-art CNN models.


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