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.

In this paper we present a framework for image-recognition based on the use of semantic content. The key idea is to compute a 3D transformation of the face image for each frame and learn a joint probability graph that maps to the same 3D data structure. The framework is simple to implement but the main idea is to learn the joint probability graph by using three state-of-the-art deep neural networks (DNNs) in conjunction with a CNN, and the framework is then implemented using deep convolutional layers. We evaluate four DNNs and three LSTMs to classify each frame, and train two CNN-based models on two datasets with different resolutions and different pose. We observe that both CNN and LSTM can be utilized effectively to achieve high classification rates and that they achieve the same rate of classification compared with state-of-the-art CNNs and LSTMs.

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

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  • Modeling language learning for social cognition research: The effect of prior knowledge base subtleties

    A Comprehensive Toolkit for Deep Face RecognitionIn this paper we present a framework for image-recognition based on the use of semantic content. The key idea is to compute a 3D transformation of the face image for each frame and learn a joint probability graph that maps to the same 3D data structure. The framework is simple to implement but the main idea is to learn the joint probability graph by using three state-of-the-art deep neural networks (DNNs) in conjunction with a CNN, and the framework is then implemented using deep convolutional layers. We evaluate four DNNs and three LSTMs to classify each frame, and train two CNN-based models on two datasets with different resolutions and different pose. We observe that both CNN and LSTM can be utilized effectively to achieve high classification rates and that they achieve the same rate of classification compared with state-of-the-art CNNs and LSTMs.


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