Unsupervised Learning of Depth and Background Variation with Multi-scale Scaling


Unsupervised Learning of Depth and Background Variation with Multi-scale Scaling – This paper presents a simple model-based approach for predicting future facial poses by combining a pair of convolutional-based deep Convolutional Neural Networks (CNNs). Our approach outperforms previous models that use only a single convolutional-bijection network to achieve accurate detection of facial pose. In addition, we show that it is possible to perform a CNN to predict future pose with small training samples. The proposed approach is applicable to different applications, including face recognition, face localization, object manipulation, gesture recognition, and recognition of human head pose from multiple sources.

This paper presents a new framework for efficient and robust motion estimation in action scenes. The proposed approach is based on the first step of a spatio-temporal LSTM (STM) architecture which aims at predicting motion in time. The STM is designed to be a discriminative projection system that combines local local features and global features. The STM uses a feature-based feature fusion to achieve an improved reconstruction system (GRU) which integrates local features and global features in a shared architecture. The proposed algorithm uses a spatio-temporal approach which combines local and global features to estimate the global features while maintaining global features. The proposed method can be used to estimate the motion in both spatio-temporal and video-image sequences. A comprehensive comparison of the proposed method shows that it is competitive in many real-world tasks.

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Unsupervised Learning of Depth and Background Variation with Multi-scale Scaling

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  • Learning to Evaluate Sentences using Word Embeddings

    On-the-fly LSTM for Action Recognition on Video via a Spatio-Temporal AlgorithmThis paper presents a new framework for efficient and robust motion estimation in action scenes. The proposed approach is based on the first step of a spatio-temporal LSTM (STM) architecture which aims at predicting motion in time. The STM is designed to be a discriminative projection system that combines local local features and global features. The STM uses a feature-based feature fusion to achieve an improved reconstruction system (GRU) which integrates local features and global features in a shared architecture. The proposed algorithm uses a spatio-temporal approach which combines local and global features to estimate the global features while maintaining global features. The proposed method can be used to estimate the motion in both spatio-temporal and video-image sequences. A comprehensive comparison of the proposed method shows that it is competitive in many real-world tasks.


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