Learning Deep Neural Networks for Multi-Person Action Hashing


Learning Deep Neural Networks for Multi-Person Action Hashing – We propose an unsupervised method to learn a classifier by performing inference on a small number of labeled instances. The inference task consists of solving a sequence-to-sequence problem, which requires multiple instances to learn to be related. We propose a deep learning approach, named as a ConvNet, that does not model a fixed feature representation, and which is not limited to a fixed feature representation. Our key contribution is to learn a new feature representation by maximizing the posterior distribution. We show that our approach can learn to predict meaningful joint distributions, and that a large number of labeled instances can be used to train the network to predict the corresponding joint distributions. Experimental results on real-world datasets demonstrate the effectiveness of our method.

We propose a novel framework for visual semantic object segmentation by incorporating deep learning models for unsupervised and deep learning models that do not explicitly provide the image or the word representation, thus leading to poor semantic segmentation results. The proposed framework provides a flexible and efficient way for image and text segmentation and for semantic segmentation in the context of supervised object segmentation. We evaluate the framework on image and text segmentation and prove that it is competitive with supervised object segmentation in terms of visual semantic segmentation performance, and outperforms the supervised and unsupervised approaches in terms of object segmentation performance.

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Learning Deep Neural Networks for Multi-Person Action Hashing

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  • A Simple Analysis of the Max Entropy Distribution

    Learning Structurally Shallow and Deep Features for Weakly Supervised Object DetectionWe propose a novel framework for visual semantic object segmentation by incorporating deep learning models for unsupervised and deep learning models that do not explicitly provide the image or the word representation, thus leading to poor semantic segmentation results. The proposed framework provides a flexible and efficient way for image and text segmentation and for semantic segmentation in the context of supervised object segmentation. We evaluate the framework on image and text segmentation and prove that it is competitive with supervised object segmentation in terms of visual semantic segmentation performance, and outperforms the supervised and unsupervised approaches in terms of object segmentation performance.


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