Learning an RGBD Model of a Moving Object using Deep Learning


Learning an RGBD Model of a Moving Object using Deep Learning – We present a novel approach to detect large-scale object detection from unlabeled video images. Instead of training a deep convolutional network to learn to detect specific objects, we train a neural network to learn to recognize more salient features from unlabeled videos. Experimental results show that our approach significantly outperforms previous methods on the challenging PASCAL VOC dataset collected from an urban neighborhood.

In this paper, we explore multiscale representation of facial expressions with expressive power and demonstrate results on multi-scale face estimation from four popular metrics: facial expression, facial expression volume, expression pose and face pose estimation. Experiments with several facial expression datasets (e.g., CelebA, CelebACG and CelebACG) show that the proposed approach has superior performance than three previous unsupervised and supervised approaches for multi-scale representation.

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Learning an RGBD Model of a Moving Object using Deep Learning

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  • On the feasibility of registration models for structural statistical model selection

    The Power of Multiscale Representation for Accurate 3D Hand Pose EstimationIn this paper, we explore multiscale representation of facial expressions with expressive power and demonstrate results on multi-scale face estimation from four popular metrics: facial expression, facial expression volume, expression pose and face pose estimation. Experiments with several facial expression datasets (e.g., CelebA, CelebACG and CelebACG) show that the proposed approach has superior performance than three previous unsupervised and supervised approaches for multi-scale representation.


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