Learning a Universal Representation of Objects


Learning a Universal Representation of Objects – We present a method for training deep network models for automatic detection of human presence and gesture motions, by solving a set of a series of image and video datasets. The purpose of this paper is to compare our method to state-of-the-art unsupervised methods on both the MNIST and DNN datasets, and compare to other unsupervised methods. This is done by using a novel hierarchical clustering scheme that consists of a global data-set of objects and a global domain-space of objects. The global data-set is used to learn a common representation from the objects, while the object-space is obtained by learning a weighted set of unlabeled images from an unseen domain-space. We show that our results on the DNN dataset outperform the current state-of-the-art unsupervised recognition methods on the MNIST and DNN datasets by a large margin.

The problem of performing temporal matching is one of high importance in many applications such as visual search, face recognition and image processing. Due to the low temporal precision of the data, it is hard to compare features. We present a new neural network architecture, which uses a Convolutional Neural Network (CNN) for retrieval of face images as a basis. Our architecture is trained on a fully-connected CNN that uses features extracted from a training set. We evaluate the model on three large-scale datasets, including 3D facial images and 2D face images. We show that our model learns to extract features from two types of data: 3D human gaze images and 2D face images. The two types of data are captured in different time steps, which makes our architecture competitive in retrieval task. The architecture achieves superior retrieval performance compared to our current state-of-the-art model while maintaining a high temporal resolution.

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Learning a Universal Representation of Objects

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  • Robust Subspace Modeling with Multi-view Feature Space Representation

    Deep Spatio-Temporal Learning of Motion RepresentationsThe problem of performing temporal matching is one of high importance in many applications such as visual search, face recognition and image processing. Due to the low temporal precision of the data, it is hard to compare features. We present a new neural network architecture, which uses a Convolutional Neural Network (CNN) for retrieval of face images as a basis. Our architecture is trained on a fully-connected CNN that uses features extracted from a training set. We evaluate the model on three large-scale datasets, including 3D facial images and 2D face images. We show that our model learns to extract features from two types of data: 3D human gaze images and 2D face images. The two types of data are captured in different time steps, which makes our architecture competitive in retrieval task. The architecture achieves superior retrieval performance compared to our current state-of-the-art model while maintaining a high temporal resolution.


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