Phase-coding structured light is an important technique in 3 D ***,a great challenge is the wrapped phase that causes geometry *** unwrapping methods such as spatial and temporal approaches face the problem of error p...
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Phase-coding structured light is an important technique in 3 D ***,a great challenge is the wrapped phase that causes geometry *** unwrapping methods such as spatial and temporal approaches face the problem of error propagation and low *** this paper,we propose to solve the phase unwrapping problem with a deep neural *** be specific,the phase unwrapping problem is cast to a semantic segmentation task,where the wrapped phase is the input and the fringe index for every pixel is the *** encoderdecoder architecture,which is like U-net,is adopted as the *** further propose a combined loss function by considering cross entropy loss,phase consistency loss and edge consistency *** 10000 artificially synthesized samples,the proposed method converges *** results demonstrate that the trained model well predicts fringe orders on both simulation data and real captured *** addition,it unwrapps every pixel independently and avoids phase error propagation,and further achieves accurate 3 D reconstruction.
Table-to-text generation involves using natural language to describe a table which has formal structure and valuable information. This paper introduces a two-level encoder-decoder neural model for table-to-text genera...
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Table-to-text generation involves using natural language to describe a table which has formal structure and valuable information. This paper introduces a two-level encoder-decoder neural model for table-to-text generation. To make the most of the structure which ordinarily is expressed as a set of field-value records and deal with rare words appearing in a table, this study adopts an improved encoder-decoder approach and uses field information to reprocess words in texts as decoding result. In encoder, two LSTM-RNNs used for combining fields and values that one LSTM-RNN gives priority to fields and the other gives first place to values. In decoder, two-level attention mechanism used on states encoded before to get the relation between words in the text and fields in the table and the relation between words in the text and values in the table. At last the decoding result is transformed to real words. The model is experimented on WIKIBIO and WEATHERGOV, and improves the current stateof-the-art BLEU-4 score from 44.89 to 45.77, and from 61.01 to 62.89 respectively.
This paper presents a character-level encoder-decoder mod-eling method for question answering(QA)from large-scale knowledge bases(KB).This method improves the existing approach [9] from three ***,long short-term memor...
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ISBN:
(纸本)9783319690049
This paper presents a character-level encoder-decoder mod-eling method for question answering(QA)from large-scale knowledge bases(KB).This method improves the existing approach [9] from three ***,long short-term memory(LSTM)structures are adopted to replace the convolutional neural networks(CNN)for encoding the can-didate entities and ***,a new strategy of generating neg-ative samples for model training is ***,a data augmentation strategy is applied to increase the size of the training set by generating factoid questions using another trained encoder-decoder ***-mental results on the SimpleQuestions dataset and the Freebase5M KB demonstrates the effectiveness of the proposed method,which improves the state-of-the-art accuracy from 70.3%to 78.8%when augmenting the training set with 70,000 generated triple-question pairs.
As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis a...
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ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve the problems. It is available in a structured or unstructured format. Therefore, in order to get corresponding data as per the application's purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers makes it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that it should not lose its context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research.
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