Robust Particle Induced Superpixel Classifier via the Hybrid Stochastic Graphical Model and Bayesian Model


Robust Particle Induced Superpixel Classifier via the Hybrid Stochastic Graphical Model and Bayesian Model – We provide an in-depth analysis of the model learning problems in both single-model and multiple-model optimization. In particular, we demonstrate the effectiveness of our approach on several benchmark datasets, which show that our model is effective, in many cases, even without the use of any additional features on the data.

We propose a novel deep learning framework for solving a multi-instance visual system task, called multi-instance object segmentation (MID). The MID framework is a joint convolutional recurrent network (CNN), a convolutional hierarchical recurrent network (CNN) and an input-to-output network (IRNN). By combining the proposed CNN and IRNN, the MID framework achieves very accurate segmentation in an unsupervised manner. Compared to previous works, these two networks are able to produce very low performance with high accuracy, even when the MID is not connected with the CNN and IRNN. In the paper, we focus on the problem of multi-instance object segmentation via IRNN and MID with a common approach to jointly training the networks. We also show the efficiency of the proposed framework to jointly improve the segmentation performance and the performance of the CNN models for the visual system segmentation task.

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Robust Particle Induced Superpixel Classifier via the Hybrid Stochastic Graphical Model and Bayesian Model

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    Learning Image Representation for Complex ProblemsWe propose a novel deep learning framework for solving a multi-instance visual system task, called multi-instance object segmentation (MID). The MID framework is a joint convolutional recurrent network (CNN), a convolutional hierarchical recurrent network (CNN) and an input-to-output network (IRNN). By combining the proposed CNN and IRNN, the MID framework achieves very accurate segmentation in an unsupervised manner. Compared to previous works, these two networks are able to produce very low performance with high accuracy, even when the MID is not connected with the CNN and IRNN. In the paper, we focus on the problem of multi-instance object segmentation via IRNN and MID with a common approach to jointly training the networks. We also show the efficiency of the proposed framework to jointly improve the segmentation performance and the performance of the CNN models for the visual system segmentation task.


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