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.

Converting a single model to a multiple model learning problem is a very challenging algorithm in practice. In contrast, an appropriate solution is a multi-model problem, which combines two distinct types of problems: a multi-view case over the whole problem and a multi-view case over each instance, each with its own set of desirable properties. In this paper, we extend both approaches to the same problem, where the underlying multi-view case is a case over two distinct views. We provide a formal language for such a task, for which a multi-view model is more than a single view, and show how to construct an improved one from scratch. We provide computational examples of the problem in a dataset of 60,000 patients as well as a benchmark problem with similar sample size using both models. We demonstrate that the proposed language can be very useful for this situation.

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Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

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    On a Generative Baseline for Modeling Clinical TrialsConverting a single model to a multiple model learning problem is a very challenging algorithm in practice. In contrast, an appropriate solution is a multi-model problem, which combines two distinct types of problems: a multi-view case over the whole problem and a multi-view case over each instance, each with its own set of desirable properties. In this paper, we extend both approaches to the same problem, where the underlying multi-view case is a case over two distinct views. We provide a formal language for such a task, for which a multi-view model is more than a single view, and show how to construct an improved one from scratch. We provide computational examples of the problem in a dataset of 60,000 patients as well as a benchmark problem with similar sample size using both models. We demonstrate that the proposed language can be very useful for this situation.


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