Stereoscopic Video Object Parsing by Multi-modal Transfer Learning


Stereoscopic Video Object Parsing by Multi-modal Transfer Learning – We propose a new class of 3D motion models for action recognition and video object retrieval based on visualizing objects in low-resolution images. Such 3D motion models are capable of capturing different aspects of the scene, such as pose, scale and lighting. These two aspects are not only pertinent when learning 3D object models, but could also be exploited for learning 2D objects as well. In this paper, we present a novel method called Multi-modal Motion Transcription (m-MNT) to encode spatial information in a new 3D pose space using deep convolutional neural networks. Such 3D data is used to learn both object semantic and pose variations of objects. We compare the performance of m-MNT on the challenging ROUGE 2017 dataset and the challenging 3D motion datasets such as WER and SLIDE. Our method yields competitive performance in terms of speed and accuracy; hence, the m-MNT class has a good future for action recognition.

We aim to improve the accuracy and quality of image segmentation for improving the accuracy of the classifier. This is achieved through the use of visual odometry (VA) information, which has recently appeared in several forms of natural human perception. VA is used for object-specific classification to improve the visual quality. VA is usually trained for only one object class, which might involve multiple classes of objects. By using a dataset consisting of a small number of unseen subjects, we trained a classifier to classify a single image as a distinct group of objects. In this article we examine the effectiveness of VA representation in the classification process. Using VA representation, we are able to outperform state-of-the-art methods by a large margin on the ROC-SV segmentation on a simple but large dataset. We also demonstrate that VA representation can effectively reduce the number of classes for a single image. We will present the next steps towards VA as a representation tool.

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Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

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  • Learning the Parameters of Discrete HMM Effects via Random Projections

    Video classification aided by human featuresWe aim to improve the accuracy and quality of image segmentation for improving the accuracy of the classifier. This is achieved through the use of visual odometry (VA) information, which has recently appeared in several forms of natural human perception. VA is used for object-specific classification to improve the visual quality. VA is usually trained for only one object class, which might involve multiple classes of objects. By using a dataset consisting of a small number of unseen subjects, we trained a classifier to classify a single image as a distinct group of objects. In this article we examine the effectiveness of VA representation in the classification process. Using VA representation, we are able to outperform state-of-the-art methods by a large margin on the ROC-SV segmentation on a simple but large dataset. We also demonstrate that VA representation can effectively reduce the number of classes for a single image. We will present the next steps towards VA as a representation tool.


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