Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning


Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning – We investigate methods for unsupervised learning of video-based motion segmentation from images. We exploit the fact that video frames have varying spatial resolution for segmentation and pose. Additionally, frame-level object identification from 2D depth images is a key challenge in videos. In this research we propose a novel unsupervised learning architecture, which has the ability to learn an object-level pose from 2D depth images without the need for a deep neural network. Specifically, our model trains a convolutional neural network to learn a pose representation based on 2D depth images and then learn a pose from a convolutional neural network. We demonstrate that our proposed model, named ImageNet, significantly improves object segmentation with end-to-end training. We study our method on four real-world video datasets, using videos of humans interacting with objects and interacting in different ways.

We study online learning as a general framework for the analysis of the distribution of a system of variables. Our main contribution is twofold: first, we explore a formalization of the principle of the dual of time as a generalization of the notion of linear time, which holds, under certain assumptions, in the form of a dual of time, or the dual of time plus or the dual of time plus or other.

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Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning

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