The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation


The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation – 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.

Color transfer refers to the retrieval of information from colors, similar to image retrieval, and we describe an algorithm that achieves color transfer. We use the convolutional neural network architecture with two different architectures: one for image retrieval and the other for classification. We propose a novel framework for image retrieval using convolutional neural networks, called Recurrent Convolutional Network (RCNN), which combines two architectures: first, images are retrieved using the image retrieval algorithm called Residual Generative Adversarial Network (RGAN). Second, images are retrieved from Deep Neural Networks. The proposed approach utilizes convolutional neural networks with multiple outputs (i.e., semantic image transformations, convolutional activations and hidden units), yielding the recognition performance of an RGBD image. Moreover, the proposed approach is particularly effective when compared by different color and texture modalities. Extensive experimental results on four dataset, as well as results from the U.S. Department of Housing and Urban Development, demonstrate the performance of our proposed approach.

On the Stability of Fitting with Incomplete Information

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The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation

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  • A Multi-Class Online Learning Task for Learning to Rank without Synchronization

    On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolutionColor transfer refers to the retrieval of information from colors, similar to image retrieval, and we describe an algorithm that achieves color transfer. We use the convolutional neural network architecture with two different architectures: one for image retrieval and the other for classification. We propose a novel framework for image retrieval using convolutional neural networks, called Recurrent Convolutional Network (RCNN), which combines two architectures: first, images are retrieved using the image retrieval algorithm called Residual Generative Adversarial Network (RGAN). Second, images are retrieved from Deep Neural Networks. The proposed approach utilizes convolutional neural networks with multiple outputs (i.e., semantic image transformations, convolutional activations and hidden units), yielding the recognition performance of an RGBD image. Moreover, the proposed approach is particularly effective when compared by different color and texture modalities. Extensive experimental results on four dataset, as well as results from the U.S. Department of Housing and Urban Development, demonstrate the performance of our proposed approach.


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