Learning Discriminative Models of Image and Video Sequences with Gaussian Mixture Models


Learning Discriminative Models of Image and Video Sequences with Gaussian Mixture Models – We explore learning neural models for image classification and semantic segmentation from the semantic segmentation of large images (e.g., the MNIST and MIMIC databases). We use Deep-CNN to build a deep neural network with a fully convolutional architecture. We then learn a novel, parallel network to train CNNs from the large datasets. We show that using a parallel CNN with a fully convolutional architecture improves classification accuracy and speed. Our proposed model is fully convolutional. We validate with a MNIST dataset. The best result from this validation is an overall improvement of 0.6 dB on the MNIST and an accuracy of 0.8 dB on those MIMIC datasets.

In this paper, we propose a new framework for fully convolutional and unsupervised multi-modal vision that simultaneously leverages the information from the input image as well as the contextual information for learning the joint features. Firstly, a deep learning framework is proposed to achieve this in two steps: i) to extract the spatial relationships of the input images, i) to simultaneously learn a common feature from the input image, and ii) to jointly learn the features from both of the two images. Then, a supervised multi-modal image generation method is implemented to extract the contextual information. Experimental results from experiments show that our method outperforms the existing joint feature-wise CNN methods and achieves significant improvements in performance compared to the state-of-the-art multi-modal approaches.

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Learning Discriminative Models of Image and Video Sequences with Gaussian Mixture Models

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    Dense2Ad: Densely-Supervised Human-Object Interaction with Deep Convolutional Neural NetworksIn this paper, we propose a new framework for fully convolutional and unsupervised multi-modal vision that simultaneously leverages the information from the input image as well as the contextual information for learning the joint features. Firstly, a deep learning framework is proposed to achieve this in two steps: i) to extract the spatial relationships of the input images, i) to simultaneously learn a common feature from the input image, and ii) to jointly learn the features from both of the two images. Then, a supervised multi-modal image generation method is implemented to extract the contextual information. Experimental results from experiments show that our method outperforms the existing joint feature-wise CNN methods and achieves significant improvements in performance compared to the state-of-the-art multi-modal approaches.


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