DeepFace: Learning to see people in real-time


DeepFace: Learning to see people in real-time – The task of learning to see people in an immersive game requires the player to make decisions and manipulate their environment. The choice of player viewpoint is crucial in a large variety of human, virtual, cognitive and collaborative games. In the long term, we aim to learn to see people by learning a new visual feature that is useful for the user to manipulate with the ability to navigate around virtual spaces. We present a multi-view model, which is adapted to the user’s choice in the first place, and use its knowledge to represent a user’s own vision. It can use objects and objects from both their human perspective, and objects and objects from the user’s own vision. We achieve an improvement of 13.8% on average over the baseline state-of-the-art, with a mean top-1 accuracy of 83.13%.

The success of deep learning is due to its ability to predict large number of points (and thus learning the entire model). We propose a novel Deep Convolutional Neural Network model that is able to learn the feature vectors of the underlying deep learning network, while retaining the ability to capture small number of point clouds. We provide a novel deep learning model for complex scene object classification and object segmentation, leveraging its ability to learn feature vectors, both at multiple scales. To this end, we train Deep Neural Network (DNN) models by using sparse models, which is an important technique in a wide range of practical applications. The proposed Deep Convolutional Network (DCNN) captures many important information within the convolutional layers, whereas the sparse model captures the whole scene at multiple scales and allows to scale to the larger number of points. We experimentally demonstrate that our proposed DCNN model achieves faster classification accuracy compared to state-of-the-art deep learning methods, in a large number of realistic scenes with a large number of hidden objects and many other objects.

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DeepFace: Learning to see people in real-time

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  • Machine learning algorithms and RNNs with spatiotemporal consistency

    On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point CloudsThe success of deep learning is due to its ability to predict large number of points (and thus learning the entire model). We propose a novel Deep Convolutional Neural Network model that is able to learn the feature vectors of the underlying deep learning network, while retaining the ability to capture small number of point clouds. We provide a novel deep learning model for complex scene object classification and object segmentation, leveraging its ability to learn feature vectors, both at multiple scales. To this end, we train Deep Neural Network (DNN) models by using sparse models, which is an important technique in a wide range of practical applications. The proposed Deep Convolutional Network (DCNN) captures many important information within the convolutional layers, whereas the sparse model captures the whole scene at multiple scales and allows to scale to the larger number of points. We experimentally demonstrate that our proposed DCNN model achieves faster classification accuracy compared to state-of-the-art deep learning methods, in a large number of realistic scenes with a large number of hidden objects and many other objects.


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