Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier


Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier – This paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.

In this paper, we present a new framework for speech understanding in natural language, based on the use of a deep neural network (DNN) to recognize speech phrases. The system first learns a sequence of words to encode the phrase into a vector space using a multi-level feature representation. Next, it uses a neural network to capture the semantic similarity between words, based on the word embedding space and their relation to sentence descriptions. A DNN trained on the word embedding space can recognize both sentences and phrases with higher precision than that provided for by state-of-the-art deep learning methods. Finally, we use these system to develop and test a speech recognition system able to recognize phrases like I’m just a human and I speak English and This is a question. The evaluation of the system shows that it correctly identifies more than 90% of phrases with positive speech-related annotations.

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Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier

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  • A Multilevel Image Segmentation Framework Using Statistical Estimation

    Predicting Speech Ambiguity of Linguistic Contexts with Multi-Tensor NetworksIn this paper, we present a new framework for speech understanding in natural language, based on the use of a deep neural network (DNN) to recognize speech phrases. The system first learns a sequence of words to encode the phrase into a vector space using a multi-level feature representation. Next, it uses a neural network to capture the semantic similarity between words, based on the word embedding space and their relation to sentence descriptions. A DNN trained on the word embedding space can recognize both sentences and phrases with higher precision than that provided for by state-of-the-art deep learning methods. Finally, we use these system to develop and test a speech recognition system able to recognize phrases like I’m just a human and I speak English and This is a question. The evaluation of the system shows that it correctly identifies more than 90% of phrases with positive speech-related annotations.


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