Multi-Modal Deep Convolutional Neural Networks for Semantic Segmentation


Multi-Modal Deep Convolutional Neural Networks for Semantic Segmentation – We present a method for joint learning of segmentation and recognition using deep learning. The segmentation method is the basis for several deep learning architectures to address the problem of object detection in video. As a technique, segmentation is trained using deep learning. By using CNNs for embedding and training, one achieves an object detection performance comparable to that of CNNs trained on object detectors. In contrast, the object detection performance can be measured using linear or nonlinear discriminant analysis. The segmentation method can use a combination of both linear and nonlinear discriminant analysis in order to improve the performance of the final target. We discuss our approach in the paper and propose a technique for joint learning segmentation.

Given a network of latent variables we propose a non-local model that learns the model parameters from a source random variable in the latent space, without learning the other variables themselves. We show that this method achieves better state-of-the-art results compared to other methods that have a local model learning the model parameters based on a latent random variable as well as on a non-local model learning the model parameters, and the resulting model is better performing on real-world datasets.

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Multi-Modal Deep Convolutional Neural Networks for Semantic Segmentation

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  • Feature Selection from Unstructured Text Data Using Unsupervised Deep Learning

    Learning Gaussian Graphical Models by InvertingGiven a network of latent variables we propose a non-local model that learns the model parameters from a source random variable in the latent space, without learning the other variables themselves. We show that this method achieves better state-of-the-art results compared to other methods that have a local model learning the model parameters based on a latent random variable as well as on a non-local model learning the model parameters, and the resulting model is better performing on real-world datasets.


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